Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

This paper studies a practical domain adaptative (DA) semantic segmentation problem
where only pseudo-labeled target data is accessible through a black-box model. Due to the …

The task of continual learning requires careful design of algorithms that can tackle
catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario …

This paper studies a practical domain adaptive (DA) semantic segmentation problem where
only pseudo-labeled target data is accessible through a black-box model. Due to the domain …

The presence of noisy examples in the training set inevitably hampers the performance of
out-of-distribution (OOD) detection. In this paper, we investigate a previously overlooked …

Abstract Machine learning (ML) algorithms are changing many aspects of modern life by
analyzing data, identifying patterns, and making predictive decisions across industries such …