Multiple instance learning (MIL) has been increasingly used in the classification of
histopathology whole slide images (WSIs). However, MIL approaches for this specific …

Previous knowledge distillation methods have shown their impressive performance on
model compression tasks, however, it is hard to explain how the knowledge they transferred …

Abstract Knowledge distillation is an effective approach to learn compact models (students)
with the supervision of large and strong models (teachers). As empirically there exists a …

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 …

The divergence of the Q-value estimation has been a prominent issue offline reinforcement
learning (offline RL), where the agent has no access to real dynamics. Traditional beliefs …

Recent advancements in vision-language pre-training (eg, CLIP) have enabled 2D vision
models to benefit from language supervision. However, the joint representation learning of …

Acoustic scene classification (ASC) can be helpful for creating context awareness for
intelligent robots. Humans naturally use the relations between acoustic scenes (AS) and …

Few-shot image classification aims at exploring transferable features from base classes to
recognize images of the unseen novel classes with only a few labeled images. Existing …

Convolutional neural networks (CNNs) have gained significant popularity in orthopedic
imaging in recent years due to their ability to solve fracture classification problems. A …

Recent evolution in deep learning has proven its value for CT-based lung nodule
classification. Most current techniques are intrinsically black-box systems, suffering from two …