Cho, Seungju
- Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
2022/03/17 by Junyoung Byun, Seungju Cho, Byun, Junyoung +7 · 3 citations
Computer Science · #Adversarial Robustness in Machine Learning #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG)
- Enhancing Robustness in Incremental Learning with Adversarial Training
2023/12/06 by Cho, Seungju, Lee, Hongsin, Kim, Changick · 3 citations
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences
- RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts
2022/12/07 by Jinyoung Park, Park, Jinyoung, Minseok Son +7 · 1 citation
Earth and Planetary Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Cryospheric studies and observations #FOS: Computer and information sciences #Meteorological Phenomena and Simulations #Precipitation Measurement and Analysis
- Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup
2023/05/24 by Byun, Junyoung, Kwon, Myung-Joon, Cho, Seungju +2 · 1 citation
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG)
- DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation
2019/08/14 by Seung Ju Cho, Cho, Seungju, Tae Joon Jun +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Bacillus and Francisella bacterial research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences
- Indirect Gradient Matching for Adversarial Robust Distillation
2023/12/06 by Lee, Hongsin, Cho, Seungju, Kim, Changick · 1 citation
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences