Thelossesarecompared onthreedegreesofshift(easy,moderate and hard), which is controlled by the drifted distance of Gaussian clusters. Herewediscuss the architecture chosen and the implementation details. Note that the task loss / surrogate loss function is used to update the meta-loss mϕ during meta-learning. The number of transformer layers and the hidden layers in MLP are selected from{1,2}. Wecanseethatthetask loss barely affects the learnt meta loss.

Current test-time adaptation (TT A) approaches aim to adapt a machine learning model to environments that change continuously.

Specifically, we maintain and store a set of device-shared domain knowledge vectors, which accumulates the knowledge learned from all devices during their lifelong adaptation process.

TPT does not explicitly align the pre-trained CLIP to become aware of the test sample distribution. For the effective test-time adaptation of V -L foundation models, it is crucial to bridge the distribution gap between the pre-training dataset and the downstream evaluation set for high zero-shot generalization.

Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images.

We introduce a multi-task setting for PCoTT A, which is practical and realistic, handling multiple tasks within one unified model during the continual adaptation.

We present a novel approach for test-time adaptation via online self-training, consisting of two components.