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Meta-Learning with Differentiable Convex Optimization

arXiv 1904.03758 ↗First posted 7 Apr 2019cs.CV cs.LGcited by 461

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Abstract (arXiv)

Many meta-learning approaches for few-shot learning rely on simple base learners such as nearest-neighbor classifiers. However, even in the few-shot regime, discriminatively trained linear predictors can offer better generalization. We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks. Our objective is to learn feature embeddings that generalize well under a linear classification rule for novel categories. To efficiently solve the objective, we exploit two properties of linear classifiers: implicit differentiation of the optimality conditions of the convex problem and the dual formulation of the optimization problem. This allows us to use high-dimensional embeddings with improved generalization at a modest increase in computational overhead. Our approach, named MetaOptNet, achieves state-of-the-art performance on miniImageNet, tieredImageNet, CIFAR-FS, and FC100 few-shot learning benchmarks. Our code is available at https://github.com/kjunelee/MetaOptNet.

Key facts. Cited by 461 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2019.

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Citing papers per year: 2021: 147; 2022: 128; 2023: 87; 2024: 55; 2025: 31; 2026: 13.

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2022Transformers learn in-context by gradient descentcited by 714
2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 123
2021On the Role of Neural Collapse in Transfer Learningcited by 100
2022Self-Support Few-Shot Semantic Segmentationcited by 96
2022Tutorial on amortized optimizationcited by 83
2021Cross-domain Few-shot Learning with Task-specific Adapterscited by 79
2021Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspectioncited by 77
2021Relational Embedding for Few-Shot Classificationcited by 74
2021Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learningcited by 72
2021FLEX: Unifying Evaluation for Few-Shot NLPcited by 67
2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
2021Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learningcited by 56

38 more

2021Pareto Self-Supervised Training for Few-Shot Learningcited by 52
2021Rectifying the Shortcut Learning of Background for Few-Shot Learningcited by 51
2021Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptationcited by 50
2022Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classificationcited by 50
2021CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraintscited by 49
2021Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Datacited by 49
2021Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learningcited by 49
2022Generalizing to New Physical Systems via Context-Informed Dynamics Modelcited by 49
2021Shallow Bayesian Meta Learning for Real-World Few-Shot Recognitioncited by 48
2023A Closer Look at Few-shot Classification Againcited by 46
2021Orthogonal Projection Losscited by 44
2022Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learningcited by 44
2021Learning Dynamic Alignment via Meta-filter for Few-shot Learningcited by 41
2021How Fine-Tuning Allows for Effective Meta-Learningcited by 41
2021Property-Aware Relation Networks for Few-Shot Molecular Property Predictioncited by 41
2023MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learningcited by 40
2021Few-Shot Object Detection via Association and DIscriminationcited by 39
2022Generating Representative Samples for Few-Shot Classificationcited by 39
2024Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Conceptscited by 39
2021Adaptive-Control-Oriented Meta-Learning for Nonlinear Systemscited by 38
2024Diffusion-Based Neural Network Weights Generationcited by 38
2022Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficultycited by 37
2021Partner-Assisted Learning for Few-Shot Image Classificationcited by 36
2021The Information Geometry of Unsupervised Reinforcement Learningcited by 36
2022DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptationcited by 36
2022Rethinking Generalization in Few-Shot Classificationcited by 36
2022Learning Domain Invariant Prompt for Vision-Language Modelscited by 35
2022Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactionscited by 34
2021Rapid Neural Architecture Search by Learning to Generate Graphs from Datasetscited by 33
2021On the Convergence Theory for Hessian-Free Bilevel Algorithmscited by 33
2021Learning where to learn: Gradient sparsity in meta and continual learningcited by 33
2022A Review of Emerging Research Directions in Abstract Visual Reasoningcited by 33
2021Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifiercited by 32
2023Leveraging sparse and shared feature activations for disentangled representation learningcited by 32
2022Few-Shot Classification with Contrastive Learningcited by 31
2021On the Power of Multitask Representation Learning in Linear MDPcited by 30
2022A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biasescited by 30
2023Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinementcited by 30

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