Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

The pre-training-then-fine-tuning paradigm has been widely used in deep learning. Due to
the huge computation cost for pre-training, practitioners usually download pre-trained …

Machine learning models have made many decision support systems to be faster, more
accurate, and more efficient. However, applications of machine learning in network security …

Deep learning relies on learning from extensive data to generate prediction results. This
approach may inadvertently capture spurious correlations within the data, leading to models …

Abstract Model adaptation aims at solving the domain transfer problem under the constraint
of only accessing the pretrained source models. With the increasing considerations of data …

Reliable and robust evaluation methods are a necessary first step towards developing
machine learning models that are themselves robust and reliable. Unfortunately, current …

Synaptic plasticity plays a critical role in the expression power of brain neural networks.
Among diverse plasticity rules, synaptic scaling presents indispensable effects on …

Causal Neural Network models have shown high levels of robustness to adversarial attacks
as well as an increased capacity for generalisation tasks such as few-shot learning and rare …

Adversarial patch attacks in the physical world are a major threat to the application of deep
learning. However, current research on adversarial patch defense algorithms focuses on …

Skeleton-based action recognition has recently made significant progress. However, data
imbalance is still a great challenge in real-world scenarios. The performance of current …