Search Results for author:
Found 3 papers, 1 papers with code
A Tale of Two Models: Constructing Evasive Attacks on Edge Models
In this paper, we introduce a new evasive attack, DIVA, that exploits these differences in edge adaptation, by adding adversarial noise to input data that maximizes the output difference between the original and adapted model.
Class-Aware Robust Adversarial Training for Object Detection
Meanwhile, instead of normalizing the total loss with the number of objects, the proposed approach decomposes the total loss into class-wise losses and normalizes each class loss using the number of objects for the class.
Mitigating Asymmetric Nonlinear Weight Update Effects in Hardware Neural Network based on Analog Resistive Synapse
Asymmetric nonlinear weight update is considered as one of the major obstacles for realizing hardware neural networks based on analog resistive synapses because it significantly compromises the online training capability.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
