117 citations · 443 across the 36 of their papers we have counts for
7 papers · 1 filter
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising
Sungmin Cha, Seonwoo Min, Sungroh Yoon +1
We improve the recently developed Neural DUDE, a neural network-based adaptive discrete denoiser, by combining it with the supervised learning framework. Namely, we make the superv…
Learning Fair Classifiers with Partially Annotated Group Labels
Sangwon Jung, Sanghyuk Chun, Taesup Moon
Recently, fairness-aware learning have become increasingly crucial, but most of those methods operate by assuming the availability of fully annotated demographic group labels. We e…
Observations on K-image Expansion of Image-Mixing Augmentation for Classification
Joonhyun Jeong, Sungmin Cha, Youngjoon Yoo +3
Image-mixing augmentations (e.g., Mixup and CutMix), which typically involve mixing two images, have become the de-facto training techniques for image classification. Despite their…
Fair Feature Distillation for Visual Recognition
Sangwon Jung, Donggyu Lee, Taeeon Park +1
Fairness is becoming an increasingly crucial issue for computer vision, especially in the human-related decision systems. However, achieving algorithmic fairness, which makes a mod…
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations
Sungmin Cha, Naeun Ko, Youngjoon Yoo +1
We propose a novel and effective purification based adversarial defense method against pre-processor blind white- and black-box attacks. Our method is computationally efficient and…
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
Sungmin Cha, Beomyoung Kim, Youngjoon Yoo +1
This paper introduces a solid state-of-the-art baseline for a class-incremental semantic segmentation (CISS) problem. While the recent CISS algorithms utilize variants of the knowl…