Gradient Alignment with Prototype Feature for Fully Test-time Adaptation

TTA guidance from entropy minimization focuses on adapting a model during the inference phase, using loss from misclassified pseudo label. We developed only the test data that is streamed online, without access to a gradient alignment loss to precisely manage the training data or test labels. Common strategies employed in adaptation process, ensuring that changes made for TTA include objectives like entropy minimization [Wang et al., some data don't negatively impact the model's performance 2021] or cross-entropy with pseudo-labels [Goyal et al., 2022], on other data. We introduce a prototype designed to guide the model's self-supervision. However, feature of a class as a proxy measure of the negative these methods are susceptible to confirmation bias [Arazo et impact. To make GAP regularizer feasible under al., 2020], where data with noisy predictions can lead the the TTA constraints, where model can only access model to continually learn in the wrong direction.