Boosting Transductive Few-Shot Fine-tuning with Margin-based Uncertainty Weighting and Probability Regularization

Abstract

Few-Shot Learning (FSL) has been rapidly developed in recent years, potentially eliminating the requirement for significant data acquisition. Few-shot fine-tuning has been demonstrated to be practically efficient and helpful, especially for out-of-distribution datum [7, 13, 17, 29]. In this work, we first observe that the few-shot fine-tuned methods are learned with the imbalanced class marginal distribution, leading to imbalanced per-class testing accuracy. This observation further motivates us to propose the Transductive Fine-tuning with Margin-based uncertainty weighting and Probability regularization (TF-MP), which learns a more balanced class marginal distribution as shown in Fig. 1. We first conduct sample weighting on unlabeled testing data with margin-based uncertainty scores and fur-ther regularize each test sample's categorical probability. TF-MP achieves state-of-the-art performance on in-/out-of-distribution evaluations of Meta- Dataset [31] and sur-passes previous transductive methods by a large margin.