Label Calibration in Source Free Domain Adaptation

TL;DR

This paper introduces a novel source-free domain adaptation method that uses evidential deep learning and softmax calibration to improve pseudolabel quality, leading to better adaptation performance.

Contribution

It proposes a new approach combining evidential deep learning and softmax calibration for pseudolabel refinement in source-free domain adaptation.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Improves pseudolabel reliability through uncertainty estimation

Enhances adaptation accuracy with calibrated softmax

Abstract

Source-free domain adaptation (SFDA) utilizes a pre-trained source model with unlabeled target data. Self-supervised SFDA techniques generate pseudolabels from the pre-trained source model, but these pseudolabels often contain noise due to domain discrepancies between the source and target domains. Traditional self-supervised SFDA techniques rely on deterministic model predictions using the softmax function, leading to unreliable pseudolabels. In this work, we propose to introduce predictive uncertainty and softmax calibration for pseudolabel refinement using evidential deep learning. The Dirichlet prior is placed over the output of the target network to capture uncertainty using evidence with a single forward pass. Furthermore, softmax calibration solves the translation invariance problem to assist in learning with noisy labels. We incorporate a combination of evidential deep learning loss and information maximization loss with calibrated softmax in both prior and non-prior target knowledge SFDA settings. Extensive experimental analysis shows that our method outperforms other state-of-the-art methods on benchmark datasets.