Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with
only unlabeled test data streams. Most of the previous TTA methods have achieved great …

Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten
by new incoming information while important, frequently used knowledge is prevented from …

Computer vision models learn to perform a task by capturing relevant statistics from training
data. It has been shown that models learn spurious age, gender, and race correlations when …

A major open problem on the road to artificial intelligence is the development of
incrementally learning systems that learn about more and more concepts over time from a …

Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …

A fundamental assumption of most machine learning algorithms is that the training and test
data are drawn from the same underlying distribution. However, this assumption is violated …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Modern deep learning approaches have achieved great success in many vision applications
by training a model using all available task-specific data. However, there are two major …