HiFE: Hierarchical Feature Ensemble Framework for Few-Shot Hypotheses Adaptation

Transferring knowledge from a source domain to a target domain in the absence of source data constitutes a formidable obstacle within the field of source-free domain adaptation, often termed hypothesis adaptation. Conventional methodologies have depended on a robustly trained (strong) source hypothesis to encapsulate the knowledge pertinent to the source domain. However, this strong hypothesis is prone to overfitting the source domain, resulting in diminished generalization performance when applied to the target domain. To mitigate this issue, we advocate for the augmentation of transferable source knowledge via the integration of multiple (weak) source models that are underfitting. Furthermore, we propose a novel architectural framework, designated as the Hierarchical Feature Ensemble (HiFE) framework for Few-Shot Hypotheses Adaptation, which amalgamates features from both the strong and intentionally underfit source models. Empirical evidence from our experiments indicates that these weaker models, while not optimal within the source domain context, contribute to an enhanced generalization capacity of the resultant model for the target domain. Moreover, the HiFE framework we introduce demonstrates superior performance, surpassing other leading baselines across a spectrum of few-shot hypothesis adaptation scenarios.