This paper rethinks meta-learning from a ‘learning’ lens, proposing TRLearner, a plug-and-play method that leverages task relations to calibrate optimization, demonstrating significant performance improvements across regression, classification, drug activity, pose prediction, and OOD generalization tasks.
Meta-Learning, Few-Shot Learning, Transfer Learning, Classification, Regression, Generalization
Jingyao Wang, Wenwen Qiang, Changwen Zheng, Hui Xiong, Gang Hua
National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China, Thrust of Artificial Intelligence, Hong Kong University of Science and Technology, Guangzhou, China, Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China, Amazon.com, Inc., Bellevue, WA, USA
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Background Problem
Meta-learning, often termed ‘learning to learn,’ aims to acquire general knowledge from multiple tasks to adapt to unseen tasks effectively. Traditional approaches, particularly those based on bi-level optimization, focus on learning a well-generalized model initialization that can be fine-tuned for specific tasks via one-step gradient descent. However, this method reveals a gap between theoretical expectations and practical implementation, often leading to underfitting or overfitting depending on task complexity. The paper addresses this by rethinking meta-learning from a ‘learning’ lens, proposing that the model should output task-specific models directly, and introduces a method to mitigate modeling errors by leveraging task relations to focus on important features, thus improving generalization and performance across diverse tasks.
Method
The paper proposes a novel perspective on meta-learning by modeling the meta-learning model Fθ as comprising model initialization layers and a nonlinear ‘meta-layer’ implemented via gradient optimization, reducing parameter count while enhancing representational capacity. The key innovation is the Task Relation Learner (TRLearner), a plug-and-play method that calibrates meta-learning optimization by leveraging task relations. TRLearner operates in two steps: (1) Extracting a task relation matrix using a learnable multi-headed similarity layer on task-specific meta-data obtained via an adaptive sampler, where similarity between tasks is computed as an average cosine similarity across multiple heads (see Eq. 4 in the paper); (2) Applying a relation-aware consistency regularization term (L_TR) during optimization to ensure that task-specific models for similar tasks produce similar outputs, thus focusing on important features (see Eq. 5 and Eq. 6). This approach integrates into the bi-level optimization framework without altering the data or core model structure, aiming to balance modeling errors caused by varying task complexities.
Experiment
The experiments are conducted across multiple domains including regression (Sinusoid and Harmonic datasets), image classification (miniImagenet, Omniglot, CUB, Places), drug activity prediction (pQSAR dataset), pose prediction (Pascal 3D dataset), and out-of-domain (OOD) generalization (Meta-Dataset). The setup evaluates TRLearner integrated with various meta-learning baselines like MAML, ProtoNet, ANIL, MetaSGD, and T-NET, comparing against state-of-the-art (SOTA) methods and regularizers. Results show consistent performance improvements: for instance, on regression tasks, TRLearner reduces MSE by up to 0.040 compared to SOTA under MAML; in image classification, it improves accuracy by nearly 7% on base models and 2% over SOTA plug-ins; similar gains are observed in drug activity and pose prediction tasks. The OOD generalization tests on Meta-Dataset show a significant 4% average accuracy boost. The experimental design is comprehensive, covering diverse task types and settings (e.g., few-shot and cross-domain learning), and the use of adaptive sampling for task selection is reasonable to ensure discriminative data. However, while results generally match the expectation of improved generalization via task relations, the paper lacks detailed discussion on failure cases or scenarios where task relations might be misleading. Ablation studies confirm the effectiveness of the regularization term and the adaptability of the task relation matrix, though computational overhead, while claimed to be minimal, needs more quantitative analysis compared to baselines.
Further Thoughts
The concept of leveraging task relations in TRLearner opens up intriguing possibilities for dynamic adaptation in meta-learning frameworks, particularly in scenarios where task distributions evolve over time, such as in continual learning settings. An insightful connection could be drawn to multi-task learning literature, where shared representations across tasks have been shown to improve performance—could TRLearner’s task relation matrix be adapted to weigh contributions from historical tasks in a lifelong learning context? Additionally, the paper’s focus on important features via task similarity resonates with recent advances in contrastive learning, where feature alignment across similar instances boosts robustness; exploring a hybrid approach combining TRLearner with contrastive objectives might further enhance generalization, especially for OOD tasks. A critical concern remains the scalability of the multi-headed similarity layer as the number of tasks grows—could hierarchical or clustering-based methods for task grouping reduce computational complexity while maintaining effectiveness? These thoughts suggest that while TRLearner is a significant step forward, its integration with other learning paradigms and optimization for real-world scalability warrant further investigation.