JEPA-TTA -Test-Time Adaptation via Latent Prediction for Domain-Shifted Medical Images

Contributions
The main contributions of this study are as follows:
• We propose JEPA-TTA, a single-image test-time adap-
tation framework that introduces latent-space predictive
learning as an adaptation objective for cross-domain
medical image classification.
• We develop a masked-view adaptation mechanism that
combines latent predictive consistency with entropy reg-
ularization, while restricting parameter updates to the
predictor and LayerNorm affine parameters.
• We systematically evaluate the proposed framework
against Source-Only, AdaBN, TENT, SHOT, and MEMO
under a CheXpert-to-COVID-19 Radiography Database
domain shift.
• We conduct component-level ablation experiments to
investigate the contributions of JEPA pretraining, en-
tropy regularization, restricted parameter updating, and
the number of target views.
• We analyze both discriminative performance and feature-
space representations to characterize the behavior of
JEPA-TTA under the evaluated domain shift.