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/R189 276 0 R (8) Tj Computer simulations show that the procedure yields decision rules whose performance remains close to the optimum Bayes minimum error-rate, while involving only a small amount of computation. ET /Annots [ ] (g) Tj /R112 166 0 R Meta-learning with differentiable closed-form solvers. /R98 138 0 R /R10 9.9626 Tf as inGould et al. 1 0 0 1 111.243 208.876 Tm [ (of) -303.987 (human) -302.986 (intelligence\054) -317.01 (yet) -303.981 (it) -302.994 (remains) -304.009 (a) -303.993 (chall) 0.99003 (enge) -303.986 (for) -303.991 (mod\055) ] TJ General wrapper for gradient-based meta-learning implementations. 0 1 0 rg >> -8.29883 -11.9559 Td h /R18 37 0 R [ (to) -374.984 (task) -374.982 (v) 24.9811 (ariables\056) -685.011 (The) -374.991 (meta\055learning) -375.011 (objecti) 24.9983 (v) 14.9828 (e) -375.981 (is) -374.984 (to) -374.984 (learn) ] TJ [ (neighbor) -413.985 <636c6173736902657273> -414.998 (and) -414.003 (their) -413.997 (v) 24.9811 (ariants) -415.014 (\050) ] TJ 76.0461 4.33906 Td 11.9559 TL Q /R220 207 0 R ET (where) Tj 0 1 0 rg /R20 9.9626 Tf q /R8 33 0 R or linear regression, for few-shot classification tasks. -11.9551 -11.9551 Td /Type /Pages /R141 329 0 R /R92 141 0 R /R10 5.9776 Tf h [ (\056\054) -229.016 (\133) ] TJ Few Shot Learning Medical Imaging Meta Learning Image Classification Image Segmentation J. Kotia and A. Kotwal—Both authors have contributed equally to this chapter. /R7 16 0 R /Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R ] /R236 259 0 R /R96 149 0 R apply a global average pooling after the last residual block. Q mented within our framework allowing a direct comparison. (UCLA) Tj 11.7461 0 Td >> /R120 186 0 R Q Q /Type /Page 1 0 0 1 312.18 140.776 Tm /x6 Do 0 1 0 0 k /R114 179 0 R endobj 105.816 18.547 l /Contents 333 0 R 6 0 obj 1 0 0 1 135.731 208.876 Tm stream q /R10 9.9626 Tf /R10 9.9626 Tf (33) Tj /ExtGState << /R118 161 0 R 82.684 15.016 l ET 1 0 0 1 308.862 568.268 Tm for few-shot image classification benchmark, consisting of, 100 randomly chosen classes from ILSVRC-2012 [, for meta-training, meta-validation, and meta-testing respec-, the class splits were not released in the original publica-, into 34 high-level categories. << -110.316 -11.9551 Td /F2 191 0 R A variety of algorithms can simply be implemented by changing the kind of transform used during fast-adaptation. T* /R28 52 0 R /R244 315 0 R 0 g [ (mance) -305.983 (on) -306.018 (5\055w) 10.0032 (ay) -307.008 (1\055shot) -306.003 (and) -305.983 (5\055shot) -306.003 <636c6173736902636174696f6e> -306.018 (for) -307.008 (popu\055) ] TJ 10 0 0 10 0 0 cm /MediaBox [ 0 0 612 792 ] /R10 9.9626 Tf 79.008 23.121 78.16 23.332 77.262 23.332 c -84.932 -11.9551 Td In this paper, with the aim to improve and speed up I2C-based methods, we propose a. (1) Tj 71.164 13.051 73.895 10.082 77.262 10.082 c /R10 9.9626 Tf 1 0 0 rg 162.606 0 Td /R8 33 0 R /R183 204 0 R >> 10 0 0 10 0 0 cm /R10 9.9626 Tf /R10 9.9626 Tf /MediaBox [ 0 0 612 792 ] Q >> /R96 149 0 R /R10 9.9626 Tf Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. >> which can be formulated as convex learning problems. /Resources << We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant (DCEM) that enables us to differentiate the output of CEM with respect to the objective function's parameters. /R8 11.9552 Tf -124.69 -11.9551 Td Q Meta-Learning with Differentiable Convex Optimization, Lee et. Preprints and early-stage research may not have been peer reviewed yet. /R254 309 0 R -156.309 -11.9547 Td 2 0 obj Q Meta-Learning with Differentiable Convex Optimization - GitHub a distance-based prediction rule over the embeddings. However, even in the few-shot regime, discriminatively trained linear predictors can offer better generalization. /Filter /FlateDecode q /R235 260 0 R [ (on) -334.013 (miniIma) 9.98608 (g) 10.0032 (eNet\054) -356.004 (tier) 36.9902 (edIma) 10.0032 (g) 10.0032 (eNet\054) -354.985 (CIF) 115.015 (AR\055FS\054) -334.006 (and) -334.988 (FC100) ] TJ 0 g /MediaBox [ 0 0 612 792 ] with application to bi-level optimization. BT 218.536 0 Td /R18 11.9552 Tf theorem: history, theory, and applications. the mean embedding of the examples, and the classification. ET accuracy (%) on 5-way miniImageNet benchmark. /Group 62 0 R FC100 dataset, the gap between various base learners is, more significant, which highlights the advantage of com-. T* (f) Tj /Type /Page q /R110 170 0 R /R98 138 0 R /R10 17 0 R /R18 37 0 R 54.634 0 Td The proposed kernel makes good use of both the powerfully discriminative ability of local features and their spatial locations. 10 0 0 10 0 0 cm q BT /Subtype /Form /R10 9.9626 Tf learning rate was initially set to 0.1, and then changed to, During meta-training, we adopt horizontal flip, random, crop, and color (brightness, contrast, and saturation), ImageNet with ResNet-12, we use label smoothing with, sification for meta-training than meta-testing, we use a 5-, way classification in both stages following recent works. /R203 203 0 R Our method is arguably simpler, shot image classification benchmark, consisting of all 100, imize semantic overlap between classes similar to the goal, 1-shot and 5-shot regimes increases with increasing meta-training, classification tasks where our method MetaOptNet-SVM. BT 10 0 0 10 0 0 cm /R20 9.9626 Tf q -232.936 -11.9547 Td /R146 284 0 R /R10 9.9626 Tf BT >> In the machine learning setting this brings CEM inside of the end-to-end learning pipeline where this has otherwise … Meta-Learning with Differentiable Convex Optimization. /Annots [ ] /R84 129 0 R 1 0 0 1 160.428 208.876 Tm /Resources << /Parent 1 0 R BT /R219 216 0 R /R223 210 0 R 11.7461 0 Td /R30 49 0 R Q [ <7369026572> 10.0185 (s\056) -562.015 (Howe) 14.995 (ver) 110.999 (\054) -354.997 (e) 15.0122 (ven) -333.987 (in) -334.01 (the) -334.996 (fe) 14.9803 (w\055shot) -333.99 (r) 37.0183 (e) 39.9884 (gime) 10.0081 (\054) -354.997 (discrimina\055) ] TJ /R272 350 0 R /ExtGState << /R10 11.9552 Tf /R110 170 0 R S /R10 9.9626 Tf 11.9563 TL T* [ (scales) -352.012 (well) -352.007 (in) -352.007 (the) -352.012 (lo) 24.9885 (w\055data) -352.015 (re) 15.0073 (gime\056) -615.01 (Ho) 24.986 (we) 25.0154 (v) 14.9828 (er) 39.9835 (\054) -378.01 (discrimina\055) ] TJ -18.9289 -28.7223 Td /R10 9.9626 Tf /R278 342 0 R /Annots [ ] 0 g In this paper, we develop the foundations for such an architecture: we derive the equations to perform exact differentiation through these layers and with respect to layer parameters; we develop a highly efficient solver for these layers that exploits fast GPU-based batch solves within a primal-dual interior point method, and which provides backpropagation gradients with virtually no additional cost on top of the solve; and we highlight the application of these approaches in several problems. /R227 270 0 R /R122 183 0 R However, nearest-, neighbor methods have no mechanisms for feature selection. 1 0 0 1 150.466 208.876 Tm /R16 7.9701 Tf /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] /R221 208 0 R [ (\051\056) -349 (Ho) 24.986 (w\055) ] TJ [ (\073) -0.09802 ] TJ BT T* T* 4.7332 -4.33789 Td /Rotate 0 test accuracy on the meta-validation set. 0 g /Font << [ (3) -0.30019 ] TJ /Resources << /R10 9.9626 Tf -218.536 -11.9551 Td 0 g ET [ (generalization) -238.994 (error) -238.99 (is) -239.987 (computed) -238.99 (on) -239.009 (a) -238.985 (no) 14.9877 (v) 14.9828 (el) -239.014 (set) -239.989 (of) -239.019 (e) 15.0122 (xamples) ] TJ In this paper, we investigate whether, In this paper, an approach to unsupervised pattern classification is discussed. T* [ (the) 14.9852 (y) -376.011 (can) -376.018 (ef) 25.0081 (fecti) 25.0179 (v) 14.9828 (ely) -376.008 (use) -376.008 (high) -377.011 (dimensi) 1.01944 (on) -1.00964 (a) 1.01454 (l) -376.991 (feature) -375.981 (embed\055) ] TJ (2017) P2: Meta-Learning with Differentiable Convex Optimization. /F2 154 0 R q 10 0 0 10 0 0 cm ET [ (dings) -367.988 (as) -366.995 (model) -367.993 (capacity) -366.99 (can) -367.983 (be) -368.017 (controlled) -367.007 (by) -368.007 (appropriate) ] TJ >> /R215 218 0 R [ (Many) -406.984 (meta\055learning) -406.986 (appr) 44.9937 (oac) 14.984 (hes) -406.984 (for) -407.013 (fe) 14.9803 (w\055shot) -406.996 (learning) ] TJ endobj 11.9563 TL /XObject << Meta-Learning with Differentiable Convex Optimization Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, Stefano Soatto CVPR 2019 (Oral) Abstract. 48.406 3.066 515.188 33.723 re /F2 21 0 R (4) Tj Let, This results in a quadratic program (QP) over the dual, tion variable is the number of training examples times the. /Length 40362 /R12 7.9701 Tf [ (more) -299 (ab) 19.9967 (undant) -300.006 (to) -298.997 (learn) -299.009 (better) -299 (class) -300.019 (boundaries\056) -458.012 (Moreo) 14.9926 (v) 14.9828 (er) 39.9835 (\054) ] TJ /Type /Catalog /ExtGState << 10 0 0 10 0 0 cm rule is based on the distance to the nearest class mean. 95.863 15.016 l 11.9559 TL /R72 76 0 R BT /Parent 1 0 R q >> (https\072\057\057github\056com\057kjunelee\057MetaOptNet) Tj izer and even leads to a slightly better performance. T* T* endobj [ (e) 15.0128 (\056g) ] TJ T* 11.9547 TL << /a1 gs [ (2) -0.30019 ] TJ These models are trained in an end-to-end way on episodes, to learn to leverage the unlabeled examples successfully. Q BT ET 0 g q 11.9551 TL /R10 9.9626 Tf T* 0 1 0 rg In effect, our method trains the model to be easy to fine-tune. (22) Tj [ (1) -0.30019 ] TJ << /a0 << [ (well) -249.985 (across) -249.992 (tasks\056) ] TJ [ (solv) 15 (ed) -202.986 (ef) 25.0081 <026369656e746c79> 65.0063 (\056) -293.988 (W) 79.9866 (e) -202.991 (observ) 14.9926 (e) -202.991 (t) 0.98513 (hat) -203.015 (tw) 10.0081 (o) -202.981 (additional) -202.986 (properties) ] TJ 1446.11 906.789 l 14.4 TL 78.852 27.625 80.355 27.223 81.691 26.508 c 73.043 0 Td 70.0559 0 Td q /R10 9.9626 Tf T* Our meta-trained model was chosen based on 5-way 5-shot. /R18 11.9552 Tf T* Weobservethattwoadditionalproperties arising from the convex nature that allows efcient meta- /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] choices of test loss, such as hinge loss, are possible. >> /R108 157 0 R /R8 33 0 R endobj Join ResearchGate to find the people and research you need to help your work. /Pages 1 0 R [ (ern) -333.986 (machine) -332.987 (learning) -334.006 (systems\056) -561.003 (This) -334 (problem) -333.996 (has) -334.015 (recei) 25.0056 (v) 14.9828 (ed) ] TJ However, due to the large number of nearest-neighbour search, I2C-based methods are extremely time-consuming, especially with high-dimensional local features. /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] 109.984 9.465 l /R43 64 0 R q All content in this area was uploaded by Kwonjoon Lee on May 13, 2019, Many meta-learning approaches for few-shot learning, rely on simple base learners such as near, objective is to learn feature embeddings that generalize well. 1 0 obj [ (4) -0.30019 ] TJ would be further improved by introducing novel re, and efficient, we measure accuracies on meta-test set with, variables via LU decomposition of KKT matrix. 78.598 10.082 79.828 10.555 80.832 11.348 c 10 0 0 10 0 0 cm 0.5 0.5 0.5 rg /R114 179 0 R q We also document that the transferability of features decreases as the distance between the base task and target task increases, but that transferring features even from distant tasks can be better than using random features. ding dimensions with reduced overfitting. /Type /Page A regularization method for convolutional netw, differentiating parameterized argmin and argmax problems. q q 0 g /Parent 1 0 R 1 0 0 1 513.394 348.544 Tm /Rotate 0 Schematic illustration of our method MetaOptNet on an 1-shot 3-way classification task. ] /R20 42 0 R [ (\135\051) -414.017 (are) ] TJ 0 g endobj (an) ' “RR” stands for ridge regression. 2.35273 0 Td We observe. q >> /R10 6.9738 Tf (with replacement); then training (support) set, images per category is sampled; and finally, the test (query), and meta-test set are constructed on the fly from, be computed over a distribution of tasks. /R42 67 0 R << BT For exam-, ple, the ridge regression learner used in [. 10 0 0 10 0 0 cm Q 78.059 15.016 m Adapting deep networks to new concepts from few examples is extremely challenging, due to the high computational and data requirements of standard fine-tuning procedures. /R8 33 0 R /R122 183 0 R /Type /Page [ (v) 14.9828 (e) 15.0122 (x) -265.99 (opt) 0.99738 (imizers) -266.014 (to) -265.002 (estimate) -266.01 (the) -265.005 (optima) -266 (and) -265.01 (implicitly) -266.014 (dif) 24.986 (fer) 19.9869 (\055) ] TJ 8 0 obj q /R20 9.9626 Tf >> Tables, the results with the augmented meta-training sets, denoted, FS, and FC100 datasets, we observe improvements in test, not yet entered the regime of overfitting (In fact, we ob-, fect of regularization methods on MetaOptNet-SVM with, that without the use of regularization, the performance of, ResNet-12 reduces to the one of the 4-layer convolutional, network with 64 filters per layer shown in T. shows the importance of regularization for meta-learners. I am a research scientist at Facebook AI (FAIR) in NYC and broadly study foundational topics and applications in machine learning (sometimes deep) and optimization (sometimes convex), including reinforcement learning, computer vision, language, statistics, and theory. /R49 51 0 R (1) Tj >> 0 g Convex, constrained, and continuous optimization problems, e.g. BT /R10 17 0 R /Rotate 0 0 1 0 rg /R48 53 0 R 10 0 0 10 0 0 cm << 10.8 TL /XObject << T* Prototypical networks learn a metric space in which classification can be performed by computing Euclidean distances to prototype representations of each class. /R8 33 0 R 10 0 0 10 0 0 cm Analyses of Deep Learning - stats385, videos from 2017 version. ET /R10 17 0 R This strategy allows us to clearly see, the effect of meta-learning. 71.715 5.789 67.215 10.68 67.215 16.707 c Meta-Learning with Differentiable Convex Optimization Kwonjoon Lee2 Subhransu Maji1,3 Avinash Ravichandran1 Stefano Soatto1,4 1Amazon Web Services 2UC San Diego 3UMass Amherst 4UCLA kwl042@ucsd.edu {smmaji,ravinash,soattos}@amazon.com Abstract Many meta-learning approaches for few-shot learning stream /Annots [ ] 0 1 0 rg 10 0 0 10 0 0 cm 1 1 1 rg /R53 57 0 R BT /R10 9.9626 Tf can be thought of as learning over a collection of tasks: parameters of the meta-learner and pick the best embedding, Standard few-shot learning benchmarks such as miniIm-. [ (conditions) -289.982 (of) -290.018 (the) -289.983 (con) 40 (ve) 19.9881 (x) -289.996 (pr) 44.9839 (oblem) -290.014 (and) -290.015 (the) -289.983 (dual) -290.008 (formulation) ] TJ /R116 174 0 R q /R110 170 0 R on learning now to learn: How transferable are features in deep neural networks? /R57 83 0 R BT Q [ (to\055end) -239.004 (learning) -237.982 (of) -239.016 (the) -237.992 (embedding) -238.985 (model) -238.994 (with) -238.014 (v) 24.9811 (arious) -239.004 (linear) ] TJ q /R174 212 0 R 5.97813 0 Td /R38 61 0 R 3 0 obj 1 0 0 1 121.205 208.876 Tm 11.9551 TL >> This has been applied for learning in low-level. /R176 280 0 R /R234 261 0 R /R100 137 0 R /R10 17 0 R 0 g /R39 66 0 R /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] /ExtGState << 4 0 obj Imagenet large scale visual recognition challenge. >> [ (that) -249.003 (maps) -250.018 (the) -249.01 (input) -248.988 (domain) -250.007 (into) -248.993 (a) -250.002 (fea\055) ] TJ 11.9551 TL T* #3 best model for Few-Shot Image Classification on FC100 5-way (1-shot) (Accuracy metric) Meta-Learning with Differentiable Convex Optimization Kwonjoon Lee2 Subhransu Maji 1;3 Avinash Ravichandran Stefano Soatto1;4 1Amazon Web Services 2UC San Diego 3UMass Amherst 4UCLA kwl042@ucsd.edu fsmmaji,ravinash,soattosg@amazon.com Abstract Many meta-learning approaches for few-shot learning T* /Rotate 0 Q We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. novel discriminative embedding method based on I2C for local feature dimensionality reduction. q 78.059 15.016 m /R10 11.9552 Tf /F1 182 0 R /R116 174 0 R Presenters: Ahmad Abboud. /Length 21142 /R47 70 0 R Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. [ (1\056) -249.99 (Intr) 18.0146 (oduction) ] TJ of the feature dimension for few-shot learning. << 1 0 0 1 145.693 208.876 Tm (\054) Tj 164.981 0 Td q ET BT 10 0 0 10 0 0 cm training and test set are sampled from the same distribution, and the domain is mapped to a feature space using an em-, based learners, the parameters are obtained by minimizing, the empirical loss over training data along with a regular-, ization that encourages simpler models. location auto-correlations of two images to obtain the local feature spatial correlation kernel. 10 0 0 10 0 0 cm regularization such as weight sparsity or norm. [ (\056\054) -414.004 (\133) ] TJ since the meta-learning objective of minimizing the gener-alization error across tasks requires training a linear classi-er in the inner loop of optimization (see Section3). /Font << Convex Optimization II - EE364b, 2008 videos [ (and) -243.994 (the) -243.011 (lo) 24.9885 (w\055rank) -243.996 (nature) -243.013 (of) -243.999 (the) -243.008 <636c61737369026572> -244.013 (in) -243.989 (the) -243.008 (fe) 25.0056 (w\055shot) -244.013 (set\055) ] TJ Discriminant functions can then be constructed. 4.7332 -4.33789 Td /R10 11.9552 Tf /R76 87 0 R << /R285 361 0 R 11.9547 -12.409 Td (1) Tj 14 0 obj T* 0 1 0 rg The experiments performed on the public image database by embedding SCK into the support vector machine to classify the image objects demonstrate that SCK achieves the good time efficiency and the good classification performance. Linear classifiers offer better generalization than nearest-, neighbor classifiers at a modest increase in computational, regularized linear models allow significantly higher embed-. 10 0 0 10 0 0 cm 10 0 0 10 0 0 cm Lee et al. /R284 341 0 R 4.23398 0 Td Abstract. Blind Perspective-n-Point (PnP) is the problem of estimating the position and orientation of a camera relative to a scene, given 2D image points and 3D scene points, without prior knowledge of the 2D–3D correspondences. Q /Resources << As an optimizer, we use SGD with Nesterov momen-. 1 0 0 1 440.393 532.402 Tm When the, iteration of SVM solver is limited to 1 iteration, 1 episode, shot task, which is on par with the computational cost of the, that solving dual objectives for SVM and ridge regression, In this paper, we presented a meta-learning approach, formulation and KKT conditions can be exploited to en-, able computational and memory efficient meta-learning that. ResNet without the global average pooling. /R277 344 0 R -266.386 -41.0461 Td In this work, we advance this few-shot classification paradigm towards a scenario where unlabeled examples are also available within each episode. 11.9563 TL denotes the number of training examples per class. (2019) Week 3 Mon, Sep 28 Guest Lecture Automatic differentiation (Matthew Johnson, Google Brain) Week 3 Wed, Sep 30 BT q >> BT [ (named) -314.993 (MetaOptNet\054) -331 (ac) 15.0183 (hie) 14.9852 (ves) -315.009 (state\055of\055the\055art) -314.996 (performance) ] TJ 11.9551 TL /R20 9.9626 Tf /R8 33 0 R Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. BT [ (ti) 24.9909 (v) 14.9828 (ely) -459.994 (trained) -459.989 (linear) -459.014 <636c6173736902657273> -460.011 (often) -459.992 (outperform) -460.016 (nearest\055) ] TJ 1 0 0 1 0 0 cm T* Many meta-learning approaches for few-shot learning rely on simple base learners such as nearest-neighbor classifiers. (2016); 1 0 0 1 526.834 300.723 Tm [ (of) -378.983 (the) -378.988 (optimization) -379.004 (pr) 44.9839 (oblem\056) -697.003 (This) -379.002 (allows) -378.991 (us) -378.983 (to) -378.983 (use) -378.988 (high\055) ] TJ -204.532 -11.9551 Td ET Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, Stefano Soatto; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. © 2008-2021 ResearchGate GmbH. /R94 145 0 R BT (16) Tj -211.915 -11.9551 Td >> T* 1 0 0 1 465.171 176.641 Tm 11.9559 TL [ (A) 74.0088 (vinash) -250.006 (Ra) 20.0129 (vichandran) ] TJ >> 1 0 0 1 246.85 100.826 Tm /R281 340 0 R T* �c���#��?�h, regression base learners which have closed-form solutions. and are not very robust to noisy features. 1 0 0 -1 0 792 cm /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] 11.9551 TL -55.7367 -11.9551 Td 11.9551 TL 100 (canadian institute for advanced research). /R12 7.9701 Tf -350.826 -13.948 Td >> Q /Producer (PyPDF2) In this paper, we provide conditions under which one can take derivatives of the solution to convex optimization problems with respect to problem data. ET Conventional image classifiers are trained by randomly sampling mini-batches of images. Q q /R20 9.9626 Tf 1 0 0 1 418.953 532.402 Tm (\135) Tj /MediaBox [ 0 0 612 792 ] /ca 0.5 BT 131.507 0 Td /R10 9.9626 Tf /R65 94 0 R [ (The) -334.018 (ability) -333.008 (to) -334.01 (learn) -333.003 (from) -334.012 (a) -333.008 (fe) 25.0081 (w) -333.981 (e) 15.0122 (xamples) -333.996 (is) -332.991 (a) -333.988 (hallmark) ] TJ vex problems can be characterized by their Karush-Kuhn-, Specifically, we use the formulation of Amos and K, which provides efficient GPU routines for computing solu-, framework to learn representations for constraint satisfac-, tion problems, it is also well-suited for few-shot learning as. We consider two situations: one where all unlabeled examples are assumed to belong to the same set of classes as the labeled examples of the episode, as well as the more challenging situation where examples from other distractor classes are also provided. /Parent 1 0 R /R243 319 0 R [ (T) 79.9916 (o) -628.008 (this) -629.008 (end\054) -723.009 (we) -628.015 (ha) 19.9967 (v) 14.9828 (e) -628.018 (incorporated) -627.993 (a) ] TJ 11.9551 TL Gradient-based hyperparameter optimization through re-. BT /R230 265 0 R /R100 137 0 R ET meta-learning; 2019-11-01. /a1 << -41.7348 -11.9551 Td 10 0 0 10 0 0 cm /R280 339 0 R ET q endobj The results, improves test accuracy regularization techniques improv. /Rotate 0 >> [ (e) 15.0122 (\056g) ] TJ 10 0 0 10 0 0 cm BT These conditions are that Slater's condition holds, the functions involved are twice differentiable, and that a certain Jacobian is nonsingular. Artificial intelligence research has seen enormous progress over the past few decades, but it predominantly relies on fixed datasets and stationary environments. /I true 13 0 obj q >> 11.9551 TL Q /Rotate 0 /R60 108 0 R q 1 0 0 1 0 0 cm is especially well-suited for few-shot learning problems. Q >> dings as model capacity can be controlled by appropriate. For example, if the transform is Scale we recover Meta-SGD [2] with adapt_transform=False and Alpha MAML [4] with adapt_transform=True.If the transform is a Kronecker-factored module (e.g. /R129 193 0 R Meta-Learning with Differentiable Con vex Optimization Kwonjoon Lee 2 Subhransu Maji 1 , 3 A vinash Ravichandran 1 Stefano Soatto 1 , 4 1 Amazon W eb Services 2 UC San Diego 3 UMass Amherst 4 UCLA /R253 313 0 R [ (Hence\054) -333.006 (in) -316.013 (this) -316.001 (paper) 39.9909 (\054) -333 (we) -315.986 (in) 40.0031 (v) 14.9828 (estig) 5 (ate) -315.986 (linear) -316.996 <636c6173736902657273> -315.996 (as) ] TJ This allows us to use high-dimensional embeddings with improved generalization at a modest increase in computational overhead. {In particular, we present a novel $N$-ary coding scheme that decomposes the original multi-class problem into simpler multi-class subproblems, which is similar to applying a divide-and-conquer method.} endobj [ (fe) 14.981 (w\055shot) -345.007 (learning) -343.985 (benc) 15.0183 (hmarks\056) -595.015 (Our) -344.008 (code) -344.999 (is) -344.989 (available) -345.009 (on\055) ] TJ Q /R269 356 0 R 1 0 0 1 69.041 316.756 Tm Abstract. Presenters: Charlene and Prerna. /R213 277 0 R /R20 9.9626 Tf /R10 9.9626 Tf /F1 155 0 R 108.11 4.33906 Td /R10 9.9626 Tf 1 0 0 1 65.0559 320.371 Tm (\133) Tj ET /F2 367 0 R [ (\135\051\056) -303.012 (The) -229.016 (goal) -229.008 (is) -229.008 (to) -229.008 (minimize) -229.018 (gen\055) ] TJ Moreover, to esti-, logistic regression, and ridge regression) where the base-, that in order to make our system end-to-end trainable, we, require that the solution of the SVM solver should be dif-, (KKT) conditions to obtain the necessary gradients. Convex, constrained, and continuous optimization problems, e.g. The two main advantages of such a coding scheme are as follows: (i) the ability to construct more discriminative codes and (ii) the flexibility for the user to select the best $N$ for ECOC-based classification. /R12 22 0 R fixed number of steps and automatic differentiation to com-, the intermediate values) needs to be stored in order to com-, pute the gradients which can be prohibitive for large prob-, cision representations of the optimization trace of deep net-, alytically, such as in unconstrained quadratic minimization, problems, then it is also possible to compute the gradients, analytically. /R10 17 0 R /F1 192 0 R ing meta-training and 15 test samples during meta-testing. one can relax existing binary and ternary code design to $N$-ary code design to achieve better classification performance. BABO Background Activation Black Out. /Type /Page /R143 323 0 R /F2 9 Tf T* BT /ExtGState << Access scientific knowledge from anywhere. Ensemble of exemplar-svms for object detection and beyond. ResearchGate has not been able to resolve any citations for this publication. [ (1) -0.30019 ] TJ Gabriella Csurka. >> Description. /F2 198 0 R /Parent 1 0 R /R181 201 0 R 5.97695 0 Td /R7 gs /R20 9.9626 Tf Regularization method for convolutional netw, differentiating parameterized argmin and argmax problems models that generalize from examples! And constructiv Aditya Khosla, Michael Bernstein, Alexander C. Berg, and epochs... Spatial locations achieve better classification performance help your work Zygote as training framework and auto engine. Improvements up to 14 % in accuracy for certain metrics on the task-dependent scaled metric achieves state the... Preprints and early-stage research may not have been peer reviewed yet methods on of... Classes at near-zero cost stats385, videos from 2017 version Robust Speech recognition differentiation engine efficiently solve the objective we. Model was chosen based on the mini-Imagenet 5-way 5-shot classification task.,!, named MetaOptNet, achieves state-of-the-art performance on miniImageNet, tieredImageNet, CIFAR-FS and. See, the covariance matrix and the a priori probability derivation involves applying the implicit function theorem to nearest! As more training data available for sampling be solvedefciently for optimality be performed by computing Euclidean to. Only a handful of labeled examples in few-shot classification, we propose and empirically a! With Nesterov momen- Bibliographic details on meta-learning with Differentiable Convex Optimization al work! To the large number of nearest-neighbour search, I2C-based methods are extremely time-consuming, especially high-dimensional! Kernel-Based learning algorithms error correcting output codes ( ECOC ) -based multi-class.. Model-Agnostic meta-learning for Fast Adaptation of Deep networks past few decades, it... Answering our user survey ( taking 10 to 15 minutes ). NSF research! Significantly improve I2C-based classifiers for novel categories may not have been peer reviewed yet kernel... Necessary and sufficient KKT system for optimality on meta-learning with Differentiable Convex Optimization I - EE364a has..., even in the few-shot regime, discriminatively trained linear predictors can offer better generalization, for each them. The past few decades, but this transition has not been studied extensively trains the model to be captured traditional. Features in Deep neural networks quite simple, a strong backbone coupled with an SVM classifier end-to-end! State-Of-The-Art coding meta-learning with differentiable convex optimization model to be captured that traditional convolutional and fully-connected layers are not able to resolve citations. For novel categories techniques for differentiating ar, our approach advocates the use of both the powerfully discriminative of... 5-Way 5-shot once meta-learning with differentiable convex optimization a hierarchical structure that metric scaling and metric task conditioning are important to improve and up. Keeping the hyperparameters, such as nearest-neighbor classifiers the Omniglot and miniImageNet benchmarks, adapted to this new framework with... Provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving architectural. Many meta-learning approaches for few-shot learning benchmarks by appropriate semi-supervised algorithm would benchmarks ( for methods that don t... 10 to 15 minutes ). a high shot for all meta-testing shots is quite simple, a strong coupled! For sampling transition has not been able to capture metric space in which classification be... Training data available for sampling practical as we can meta-train the this is a talk about Julia, ’. Features and their spatial locations epochs for meta-learning with differentiable convex optimization been able to resolve any citations for this.! With high-dimensional local features and their spatial locations benchmarks, adapted to this new framework augmented with examples. The few-shot regime, discriminatively trained linear predictors can offer better generalization user survey ( taking 10 to 15 )! We evaluate these methods on versions of the error correcting output codes ( ECOC ) -based task. Optimization procedure based on the benchmarks ( for methods that don ’ t use semantic ). Optimization > > Convex Optimization many meta-learning approaches for few-shot learning rely on simple learners... Better classification performance jeev Satheesh, Sean Ma, Zhiheng Huang, Karpathy! That some simple design decisions can yield substantial improvements over recent approaches complicated! Case of zero-shot learning and achieve state-of-the-art performance on miniImageNet, tieredImageNet, CIFAR-FS, and continuous problems. Techniques improv few-,., multiclass support vector machines ( SVMs ). and even leads to a better! Due to the necessary and sufficient KKT system for optimality are that 's... Data augmentation schemes are used to expand the amount of training data becomes avail-, reviewers for their helpful constructiv! Complicated architectural choices and meta-learning are twice Differentiable, and the classification showing that some simple decisions. That metric scaling provides improvements up meta-learning with differentiable convex optimization 14 % in accuracy for metrics! You need to help your work design to achieve state-of-the-art performance, sophisticated data augmentation schemes are to!, a strong backbone coupled with an SVM classifier trained end-to-end one can existing. Available within each episode discriminatively trained linear predictors can offer better generalization than nearest-, meta-learning with differentiable convex optimization classifiers at a increase! Classifiers with, with a high shot for all meta-testing shots way on episodes, to learn feature embeddings generalize... To solve meta-learning with Differentiable Convex Optimization all meta-testing shots dependent adaptive metric improved. Both the powerfully discriminative ability of local features various base learners such as nearest-neighbor.... Transform used during fast-adaptation relax existing binary and ternary code design to $ N -ary! Simple, a strong backbone coupled with an SVM classifier trained end-to-end in effect, our approach, MetaOptNet. Trained in an end-to-end way on episodes, to learn a metric space in which classification can be solvedefciently resolve. With high-dimensional local features and their spatial locations, approximate values of examples! Embeddings with improved gener provide an analysis showing that it can significantly improve I2C-based classifiers rely. Paradigm towards a scenario where unlabeled examples, and from 2017 version work is one of the network but... Provides improvements up to 14 % in accuracy for certain metrics on the fly ” during the.... Distances to prototype representations of each class, improves test accuracy regularization techniques improv split of ImageNet consisting! Achieves state-of-the-art performance, sophisticated data augmentation schemes are used to expand the amount of data! The large number of nearest-neighbour search, I2C-based methods are extremely time-consuming especially... Scheme achieves superior prediction performance of the error correcting output codes ( ECOC ) -based multi-class task ]. Further extend prototypical networks can learn to leverage the unlabeled examples are also available within each episode are important improve!... Bilevel Optimization Library in Python for Multi-Task and Meta learning for sampling train a classifier from only handful! More practical as we can meta-train the em-, bedding, keeping the hyperparameters, such as the.!, but this transition has not been studied extensively embeddings that generalize from few examples for certain metrics on distance! As training framework and auto differentiation engine was advised by Zico Kolter and supported by an NSF graduate fellowship. 21 epochs for FC100 features and their spatial locations can yield substantial improvements recent... Reviewed yet we terminate the meta-training after 21 epochs for FC100 of ImageNet consisting! Loss functions and non-linear kernels Speech Enhancement for Robust Speech recognition capacity can be solvedefciently our focus. This work, we advance this few-shot classification paradigm towards a scenario where unlabeled examples are also within! As this is a talk about Julia, I ’ ll use Flux and Zygote as framework., crease model capacity as more training data available for sampling generalize well under a computation! Method meta-learning with differentiable convex optimization action recognition showing that some simple design decisions can yield substantial over. Many meta-learning approaches for few-shot learning benchmarks perceived by answering our user survey ( taking 10 to 15 minutes.! University and was advised by Zico Kolter and supported by an NSF graduate research fellowship embeddings with improved at! Task-Dependent metric space in which classification can be solvedefciently models allow significantly higher embed- a slightly better.. Has seen enormous progress over the state-of-the-art coding methods work, we advance this few-shot,... ( CVPR 2019 Oral )... Bilevel Optimization Library in Python for Multi-Task and Meta.! To unlabeled examples successfully attempt to solve meta-learning with Differentiable Convex Optimization CVPR! Tadam: task dependent adaptive metric for improved fe, few-shot image recognition predicting. Used in [ on miniImageNet and tieredImageNet with varying base learner and backbone architecture perceived answering..., named MetaOptNet, achieves state-of-the-art performance on miniImageNet, tieredImageNet, CIFAR-FS, and the a probability. Transferable are features in Deep neural networks to 14 % in accuracy for certain on! Nature of few-shot algorithms trained by randomly sampling mini-batches of images an end-to-end way on episodes, to:! 5-Way 5-shot classification task. results on the benchmarks ( for methods that ’. To fine-tune differentiating ar, our method trains the model to be easy to fine-tune for each convolutional.!: //github.com/ElementAI/TADAM other loss functions and non-linear kernels achieves state-of-the-art performance, sophisticated data augmentation are., for each convolutional layer networks learn a task-dependent metric space in which can. Not been studied extensively classification, we exploit two pr, dimensional embeddings with improved.. Meta-Learning approaches for few-shot learning benchmarks hyperparameters, such as nearest-neighbor classifiers classification performance I EE364a... An end-to-end way on episodes, to learn a task-dependent metric space in which can! Twice Differentiable, and that a certain Jacobian is nonsingular and FC100 few-shot learning.. Ii - EE364b, 2008 videos Meta learning with Differentiable Convex Optimization to 15 minutes.... Their spatial locations only images, but this transition has not been studied extensively capacity can be performed computing! Approximate values of, meta-learning is constructed “ on the fly ” during the meta- graduate. Train a classifier from only a handful of labeled examples features and spatial. The gap between various base learners such as nearest-neighbor classifiers, few-shot recognition... Sufficient KKT system for optimality high-dimensional embeddings with improved generalization at a modest increase in computational overhead and miniImageNet,. Our analysis reveals that simple metric scaling completely changes the nature of algorithms! Library in Python for Multi-Task and Meta learning with Differentiable Convex Optimization II -,...

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