4 accepted papers
The study of adversarial examples and their activations have attracted significant attention for secure and robust learning with deep neural networks (DNNs). Different from existing works, in this paper, we highlight two new characteristics of adversarial examples from the channel-wise activation p…
Convolutional neural networks (CNNs) can be easily over-fitted when they are over-parametered. The popular dropout that drops feature units randomly can't always work well for CNNs, due to the problem of under-dropping. To eliminate this problem, some structural dropout methods such as SpatialDropou…
Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting). As plenty of machine learning models have been deployed v…
Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as ""black-box"" and lack of interpretability. One main reason is due to the filter-class entanglement -- an intricate many-to-many correspondence between filters and classes. Most ex…