  Fully test-time adaptation (FTTA) adapts a model that is trained on a source
domain to a target domain during the testing phase, where the two domains
follow different distributions and source data is unavailable during the
training phase. Existing methods usually adopt entropy minimization to reduce
the uncertainty of target prediction results, and improve the FTTA performance
accordingly. However, they fail to ensure the diversity in target prediction
results. Recent domain adaptation study has shown that maximizing the sum of
singular values of prediction results can simultaneously enhance their
confidence (discriminability) and diversity. However, during the training
phase, larger singular values usually take up a dominant position in loss
maximization. This results in the model being more inclined to enhance
discriminability for easily distinguishable classes, and the improvement in
diversity is insufficiently effective. Furthermore, the adaptation and
prediction in FTTA only use data from the current batch, which may lead to the
risk of overfitting. To address the aforementioned issues, we propose
maximizing the sum of singular values while minimizing their variance. This
enables the model's focus toward the smaller singular values, enhancing
discriminability between more challenging classes and effectively increasing
the diversity of prediction results. Moreover, we incorporate data from the
previous batch to realize semantic data augmentation for the current batch,
reducing the risk of overfitting. Extensive experiments on benchmark datasets
show our proposed approach outperforms some compared state-of-the-art FTTA
methods.

Artificial Intelligence;Computer Vision and Pattern Recognition