In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Enhancing the generalization capability of deep neural networks to unseen domains is
crucial for safety-critical applications in the real world such as autonomous driving. To …

Abstract Single Domain Generalization (SDG) tackles the problem of training a model on a
single source domain so that it generalizes to any unseen target domain. While this has …

In self-supervised representation learning, a common idea behind most of the state-of-the-
art approaches is to enforce the robustness of the representations to predefined …

What does a neural network encode about a concept as we traverse through the layers?
Interpretability in machine learning is undoubtedly important, but the calculations of neural …

In this paper, we are concerned with enhancing the generalization capability of object
detectors. And we consider a realistic yet challenging scenario, namely Single-Domain …

In this paper, we study the task of synthetic-to-real domain generalized semantic
segmentation, which aims to learn a model that is robust to unseen real-world scenes using …

Abstract Domain generalization (DG) aims to learn domaingeneralizable models from one or
multiple source domains that can perform well in unseen target domains. Despite its recent …

In this paper, we consider the problem of domain generalization in semantic segmentation,
which aims to learn a robust model using only labeled synthetic (source) data. The model is …