ID-TTA: Classifier-Free Test Time Adaptation for Metric Learning

Deploying a pre-trained model on the target data can be challenging due to the domain shift between the target and the source data. Test Time Adaptation (TTA) has recently gained increasing attention, aiming to adapt the domain shift during inference without needing annotated data. However, most previous TTA methods still require the classifier trained on the source data for adaptation, entropy minimization is applied based on the classifier’s output. Therefore, previous TTA methods are not applicable for metric learning tasks, e.g., identify verification systems. In this paper, we propose IDentity-based TTA (ID-TTA), which can adapt pre-trained models for metric learning tasks without the need for the classifier. We implement two types of adaptations: (1) self-supervised, which generates different variations of any given sample and minimizes the distance of the embeddings of the variations from the embedding of the original sample. (2) pair-wise, that compares any two given samples and aims to minimize the distance between the embeddings of the two samples if the pre-trained model predicts them to be from the same identity. We evaluate ID-TTA against multiple face recognition datasets by adding varying degrees of domain shift in the image. We show that ID-TTA is notably effective in adapting the baseline models to the target domain.