An automatic vision-based sewer inspection plays a vital role of sewage system in a modern
city. Recent advances focus on modeling a deep learning-based method to realize the …

One of the challenges for neural networks in real-life applications is the overconfident errors
these models make when the data is not from the original training distribution. Addressing …

The task of out-of-distribution (OOD) detection is notoriously ill-defined. Earlier works
focused on new-class detection, aiming to identify label-altering data distribution shifts, also …

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets
but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples …

In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative
that DNNs provide inference robust to external perturbations-both intentional and …

We propose a generalized method for boosting the generalization ability of pre-trained
vision-language models (VLMs) while fine-tuning on downstream few-shot tasks. The idea is …

Out-of-distribution (OOD) detection aims to detect test samples outside the training category
space, which is an essential component in building reliable machine learning systems …

This paper presents a systematic investigation into the effectiveness of Self-Supervised
Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by …

The real-world data distribution is essentially long-tailed, which poses a significant
challenge to the deep model. Classification models minimizing cross-entropy loss struggle …

The behavior of neural networks (NNs) on previously unseen types of data (out-of-
distribution or OOD) is typically unpredictable. This can be dangerous if the network's output …