no code implementations • 10 Apr 2024 • Shangyu Chen, Zibo Zhao, YuanYuan Zhao, Xiang Li
Besides, we proposed a series of evaluation protocols for personalization: to what extend the response is personal to the different users.
no code implementations • NeurIPS 2020 • Jianda Chen, Shangyu Chen, Sinno Jialin Pan
In this paper, we propose a deep reinforcement learning (DRL) based framework to efficiently perform runtime channel pruning on convolutional neural networks (CNNs).
no code implementations • 3 Feb 2020 • Shuo Wang, Tianle Chen, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen
In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner.
no code implementations • 18 Jan 2020 • Shuo Wang, Tianle Chen, Shangyu Chen, Carsten Rudolph, Surya Nepal, Marthie Grobler
Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency.
no code implementations • 10 Jan 2020 • Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen
In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: \textit{pruning-based}, \textit{retraining-based} and \textit{input pre-processing-based defenses}.
no code implementations • 6 Jan 2020 • Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen
We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.
1 code implementation • NeurIPS 2019 • Shangyu Chen, Wenya Wang, Sinno Jialin Pan
However, these methods only heuristically make training-based quantization applicable, without further analysis on how the approximated gradients can assist training of a quantized network.
2 code implementations • NeurIPS 2017 • Xin Dong, Shangyu Chen, Sinno Jialin Pan
How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems.