most citedPhysical-layer Adversarial Robustness for Deep Learning-based Semantic Communications

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Direct Distillation between Different Domains

Jialiang Tang, Shuo Chen, Gang Niu +4

Knowledge Distillation (KD) aims to learn a compact student network using knowledge from a large pre-trained teacher network, where both networks are trained on data from the same…

cs.CV2023

Distribution Shift Matters for Knowledge Distillation with Webly Collected Images

Jialiang Tang, Shuo Chen, Gang Niu +2

Knowledge distillation aims to learn a lightweight student network from a pre-trained teacher network. In practice, existing knowledge distillation methods are usually infeasible w…

cs.IT20231 cited

Directed Message Passing Based on Attention for Prediction of Molecular Properties

Chen Gong, Yvon Maday

Molecular representation learning (MRL) has long been crucial in the fields of drug discovery and materials science, and it has made significant progress due to the development of…

eess.SP20232 cited

Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications

Guoshun Nan, Zhichun Li, Jinli Zhai +7

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential fo…

cs.LG2023

Recover Triggered States: Protect Model Against Backdoor Attack in Reinforcement Learning

Hao Chen, Chen Gong, Yizhe Wang +1

A backdoor attack allows a malicious user to manipulate the environment or corrupt the training data, thus inserting a backdoor into the trained agent. Such attacks compromise the…