Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning

Abstract

Few-shot learning, which aims at extracting new concepts rapidly from extremely few examples of novel classes, has been featured into the meta-learning paradigm recently. Yet, the key challenge of how to learn a generalizable classifier with the capability of adapting to specific tasks with severely limited data still remains in this domain. To this end, we propose a Transductive Episodic-wise Adaptive Metric (TEAM) framework for few-shot learning, by integrating the meta-learning paradigm with both deep metric learning and transductive inference. With exploring the pairwise constraints and regularization prior within each task, we explicitly formulate the adaptation procedure into a standard semi-definite programming problem. By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space. Moreover, we further leverage an attention-based bi-directional similarity strategy for extracting the more robust relationship between queries and prototypes. Extensive experiments on three benchmark datasets show that our framework is superior to other existing approaches and achieves the state-of-the-art performance in the few-shot literature.

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Cited by in corpus (16)

• BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
• Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
• Self-supervised Knowledge Distillation for Few-shot Learning
• MHFC: Multi-Head Feature Collaboration for Few-Shot Learning
• Meta-Learned Confidence for Few-shot Learning
• Meta-Learning across Meta-Tasks for Few-Shot Learning
• Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time
• Instance Credibility Inference for Few-Shot Learning
• Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
• How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
• Transductive Few-Shot Learning: Clustering is All You Need?
• A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning
• Few-shot Learning with Global Relatedness Decoupled-Distillation
• MDFM: Multi-Decision Fusing Model for Few-Shot Learning
• Mutual-Information Based Few-Shot Classification
• Cooperative Bi-path Metric for Few-shot Learning