activity
20212026
most citedBetter Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training

7 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Continuous Exposure-Time Modeling for Realistic Atmospheric Turbulence Synthesis

Junwei Zeng, Dong Liang, Sheng-Jun Huang +2

Atmospheric turbulence significantly degrades long-range imaging by introducing geometric warping and exposure-time-dependent blur, which adversely affects both visual quality and…

cs.LG2025

Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation

Xiaofeng Cao, Mingwei Xu, Xin Yu +8

Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI); however, the costs associated with data annotation and model training remain…

cs.CV2025

StructSR: Refuse Spurious Details in Real-World Image Super-Resolution

Yachao Li, Dong Liang, Tianyu Ding +1

Diffusion-based models have shown great promise in real-world image super-resolution (Real-ISR), but often generate content with structural errors and spurious texture details due…

cs.CV2024

Relative Difficulty Distillation for Semantic Segmentation

Dong Liang, Yue Sun, Yun Du +2

Current knowledge distillation (KD) methods primarily focus on transferring various structured knowledge and designing corresponding optimization goals to encourage the student net…

cs.LG2021★ 7 cited

Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training

Lue Tao, Lei Feng, Jinfeng Yi +2

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by slightly perturbing the features of correctly labeled training examples. By formalizing…