As AI systems have obtained significant performance to be deployed widely in our daily lives
and human society, people both enjoy the benefits brought by these technologies and suffer …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

The rise of deep neural networks has led to several breakthroughs for semantic
segmentation. In spite of this, a model trained on source domain often fails to work properly …

As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …

Despite the considerable advances in deep learning for object recognition, there are still
several factors that hinder the performance of deep learning models. One of these factors is …

In this work, we propose to tackle the problem of domain generalization in the context of
insufficient samples. Instead of extracting latent feature embeddings based on deterministic …

Machine learning traditionally assumes that the training and testing data are distributed
independently and identically. However, in many real-world settings, the data distribution …

Machine learning traditionally assumes that training and testing data are distributed
independently and identically. However, in many real-world settings, the data distribution …

With the heavy reliance on Information Technologies in every aspect of our daily lives,
Machine Learning (ML) models have become a cornerstone of these technologies' rapid …

Clinicians use chest radiography (CXR) to diagnose common pathologies. Automated
classification of these diseases can expedite analysis workflow, scale to growing numbers of …