Enhancing Information Maximization with Distance-Aware Contrastive
  Learning for Source-Free Cross-Domain Few-Shot Learning
Huali Xu Li Liu Shuaifeng Zhi Shaojing Fu Zhuo Su Ming-Ming Cheng Yongxiang Liu
Existing Cross-Domain Few-Shot Learning (CDFSL) methods require access to
source domain data to train a model in the pre-training phase. However, due to
increasing concerns about data privacy and the desire to reduce data
transmission and training costs, it is necessary to develop a CDFSL solution
without accessing source data. For this reason, this paper explores a
Source-Free CDFSL (SF-CDFSL) problem, in which CDFSL is addressed through the
use of existing pretrained models instead of training a model with source data,
avoiding accessing source data. This paper proposes an Enhanced Information
Maximization with Distance-Aware Contrastive Learning (IM-DCL) method to
address these challenges. Firstly, we introduce the transductive mechanism for
learning the query set. Secondly, information maximization (IM) is explored to
map target samples into both individual certainty and global diversity
predictions, helping the source model better fit the target data distribution.
However, IM fails to learn the decision boundary of the target task. This
motivates us to introduce a novel approach called Distance-Aware Contrastive
Learning (DCL), in which we consider the entire feature set as both positive
and negative sets, akin to Schrodinger's concept of a dual state. Instead of a
rigid separation between positive and negative sets, we employ a weighted
distance calculation among features to establish a soft classification of the
positive and negative sets for the entire feature set. Furthermore, we address
issues related to IM by incorporating contrastive constraints between object
features and their corresponding positive and negative sets. Evaluations of the
4 datasets in the BSCD-FSL benchmark indicate that the proposed IM-DCL, without
accessing the source domain, demonstrates superiority over existing methods,
especially in the distant domain task.