Free Lunch to Meet the Gap: Intermediate Domain Reconstruction for Cross-Domain Few-Shot Learning

TL;DR

This paper introduces a novel approach for cross-domain few-shot learning by constructing intermediate domain proxies to facilitate domain adaptation and improve performance across multiple benchmarks.

Contribution

It proposes the creation of Intermediate Domain Proxies using source features, enabling better domain alignment and feature reconstruction in CDFSL.

Findings

Surpasses state-of-the-art on 8 benchmarks

Effective intermediate domain reconstruction improves adaptation

Fast domain alignment enhances learning efficiency

Abstract

Cross-Domain Few-Shot Learning (CDFSL) endeavors to transfer generalized knowledge from the source domain to target domains using only a minimal amount of training data, which faces a triplet of learning challenges in the meantime, i.e., semantic disjoint, large domain discrepancy, and data scarcity. Different from predominant CDFSL works focused on generalized representations, we make novel attempts to construct Intermediate Domain Proxies (IDP) with source feature embeddings as the codebook and reconstruct the target domain feature with this learned codebook. We then conduct an empirical study to explore the intrinsic attributes from perspectives of visual styles and semantic contents in intermediate domain proxies. Reaping benefits from these attributes of intermediate domains, we develop a fast domain alignment method to use these proxies as learning guidance for target domain feature transformation. With the collaborative learning of intermediate domain reconstruction and target feature transformation, our proposed model is able to surpass the state-of-the-art models by a margin on 8 cross-domain few-shot learning benchmarks.