Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Recent advances have been made in applying convolutional neural networks to achieve
more precise prediction results for medical image segmentation problems. However, the …

Abstract Domain generalization studies the problem of training a model with samples from
several domains (or distributions) and then testing the model with samples from a new …

Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying
neural networks in the real world. Previous methods commonly relied on the output of a …

Federated learning is a technique that enables a centralized server to learn from distributed
clients via communications without accessing the client local data. However, existing …

Over the past decade, deep learning models have exhibited considerable advancements,
reaching or even exceeding human-level performance in a range of visual perception tasks …

Knowledge distillation (KD), as an efficient and effective model compression technique, has
received considerable attention in deep learning. The key to its success is about transferring …