Search Results for author:
Found 5 papers, 2 papers with code
Common Knowledge Learning for Generating Transferable Adversarial Examples
Specifically, to reduce the model-specific features and obtain better output distributions, we construct a multi-teacher framework, where the knowledge is distilled from different teacher architectures into one student network.
Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners
Based on digital pathology slice scanning technology, artificial intelligence algorithms represented by deep learning have achieved remarkable results in the field of computational pathology.
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization
Differentiable ARchiTecture Search (DARTS) uses a continuous relaxation of network representation and dramatically accelerates Neural Architecture Search (NAS) by almost thousands of times in GPU-day.
Ranked #9 on
Neural Architecture Search
on CIFAR-10
Exploring Transferable and Robust Adversarial Perturbation Generation from the Perspective of Network Hierarchy
In this paper, we explore effective mechanisms to boost both of them from the perspective of network hierarchy, where a typical network can be hierarchically divided into output stage, intermediate stage and input stage.
A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate
This portion of distortions, which is induced by unnecessary modifications and lack of proper perceptual distortion constraint, is the target of the proposed framework.
