37 citations · 78 across the 32 of their papers we have counts for
8 papers · 1 filter
Imitation Learning from Purified Demonstrations
Yunke Wang, Minjing Dong, Yukun Zhao +2
Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in r…
Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks
Huihui Gong, Minjing Dong, Siqi Ma +3
Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations. However, most defense mechanisms only consider a single ty…
Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization
Huihui Gong, Minjing Dong, Siqi Ma +3
Deep neural networks (DNNs) have achieved state-of-the-art performance on face recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs requires taking…
A Benchmark Study on Calibration
Linwei Tao, Younan Zhu, Haolan Guo +2
Deep neural networks are increasingly utilized in various machine learning tasks. However, as these models grow in complexity, they often face calibration issues, despite enhanced…
Neural Architecture Retrieval
Xiaohuan Pei, Yanxi Li, Minjing Dong +1
With the increasing number of new neural architecture designs and substantial existing neural architectures, it becomes difficult for the researchers to situate their contributions…
Dual Focal Loss for Calibration
Linwei Tao, Minjing Dong, Chang Xu
The use of deep neural networks in real-world applications require well-calibrated networks with confidence scores that accurately reflect the actual probability. However, it has b…
AMD: Autoregressive Motion Diffusion
Bo Han, Hao Peng, Minjing Dong +3
Human motion generation aims to produce plausible human motion sequences according to various conditional inputs, such as text or audio. Despite the feasibility of existing methods…
Calibrating a Deep Neural Network with Its Predecessors
Linwei Tao, Minjing Dong, Daochang Liu +2
Confidence calibration - the process to calibrate the output probability distribution of neural networks - is essential for safety-critical applications of such networks. Recent wo…