Over the past decade, deep learning has proven to be a highly effective tool for learning
meaningful features from raw data. However, it remains an open question how deep …

Most few-shot object detection methods aim to utilize the learned generalizable knowledge
from base categories to identify instances of novel categories. The fundamental assumption …

This paper is a preliminary study of the robustness and noise analysis of deep neural
networks via a game theory formulation Bayesian Neural Networks (BNN) and the maximal …

We consider the problem of learning maximally informative representations for data in a high-
dimensional space with distribution supported on or around a single or multiple low …

In deep learning, Bayesian neural networks (BNN) and dropout techniques provide the role
of robustness analysis, and the minimax method used to be a conservative choice in the …

Recent studies suggest that the generalization performance of neural networks is strongly
linked to their ability to learn low-dimensional data representations. However, limited …

In recent years, numerous studies have demonstrated the close connection between neural
networks' generalization performance and their ability to learn low-dimensional …