Meta-Exploiting Complementary Semantic Consistency for Cross-Domain Few-Shot Learning Promotion

Abstract

Meta-learning has emerged as an effective solver for cross-domain few-shot learning (CD-FSL) tasks. Despite achieving obvious progress recently, the typical episodic learning paradigm often causes the feature embedding model collapsing into the simplicity bias pitfall, viz., the model tends to prioritize some shortcut patterns (e.g., color, style, background) that are only sufficient to distinguish categories in source domain, while fail to generalize across domains. To mitigate this problem, we present a novel meta-learning framework which emphasizes meta-exploiting inductive bias to alleviate simplicity bias for CD-FSL promotion, and mainly contributes in the following four aspects. 1) We establish a novel inductive bias for CD-FSL, termed complementary semantic consistency (CSC). The rationale behind lies in that forcing the semantic consistency between two complementary feature learning schemes is beneficial to distill cross-domain transferable features. 2) We establish a solid theoretical foundation, supported by rigorous mathematical proofs and key lemmas, which demonstrates that CSC establishes a tighter generalization bound and facilitates the learning of domain-invariant features. 3) Inspired by CSC, we propose a general meta-learning framework, which implements complementary feature embedding models using parallel networks with the same architecture but different input forms, and introduce proper knowledge distillation losses to encourage the semantic consistency between different branches during meta-training. This framework can be seamlessly integrated with any complementary feature learning schemes. 4) To clarify this point, we instantiate two effective meta-learners based on the proposed framework. The former establishes a two-branch network that simultaneously classifies both the query image and its random local crops. The latter decomposes the query image into high-frequency and low-frequency components, which are then integrated into a parallel feature embedding network for category prediction, analogous to the original query image. Subsequently, a KL divergence based knowledge distillation loss is separately leveraged to force the prediction consistency between the complementary branches (e.g., local-global, spatial-frequency) during meta-training. By doing these, both learners are able to distill cross-domain transferable features with better generalization performance. Empirical results on diverse benchmarks consistently affirm the proposed framework’s advantages, while additional analysis provides compelling support for our key claims.