Despite their impressive performance in object recognition and other tasks under standard
testing conditions, deep networks often fail to generalize to out-of-distribution (ood) samples …

Overconfident predictions on out-of-distribution (OOD) samples is a thorny issue for deep
neural networks. The key to resolve the OOD overconfidence issue inherently is to build a …

Natural data are often long-tail distributed over semantic classes. Existing recognition
methods tackle this imbalanced classification by placing more emphasis on the tail data …

Recognizing out-of-distribution (OOD) samples is critical for machine learning systems
deployed in the open world. The vast majority of OOD detection methods are driven by a …

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying
machine learning models in the real world. Existing solutions are mainly driven by small …

Despite the success of deep neural networks, there are still many challenges in deep
representation learning due to the data scarcity issues such as data imbalance, unseen …

Learning good feature embeddings for images often requires substantial training data. As a
consequence, in settings where training data is limited (eg, few-shot and zero-shot learning) …

The conventional wisdom behind learning deep classification models is to focus on bad-
classified examples and ignore well-classified examples that are far from the decision …

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution
(OOD) detection but the lack of OOD images in the target dataset in their training can lead to …

Deep neural networks have made huge progress in the last few decades. However, as the
real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be …