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20162023
most citedRecent Advances in Adversarial Training for Adversarial Robustness

43 citations · 127 across the 21 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.CV20233 cited

ExposureDiffusion: Learning to Expose for Low-light Image Enhancement

Yufei Wang, Yi Yu, Wenhan Yang +4

Previous raw image-based low-light image enhancement methods predominantly relied on feed-forward neural networks to learn deterministic mappings from low-light to normally-exposed…

cs.CV2023

Enhancing Low-Light Images Using Infrared-Encoded Images

Shulin Tian, Yufei Wang, Renjie Wan +3

Low-light image enhancement task is essential yet challenging as it is ill-posed intrinsically. Previous arts mainly focus on the low-light images captured in the visible spectrum…

cs.CV2023

Beyond Learned Metadata-based Raw Image Reconstruction

Yufei Wang, Yi Yu, Wenhan Yang +4

While raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels, they are not widely adopted by general users due to their substant…

cs.CV2023

WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes

Satoshi Tsutsui, Winnie Pang, Bihan Wen

The examination of blood samples at a microscopic level plays a fundamental role in clinical diagnostics, influencing a wide range of medical conditions. For instance, an in-depth…

cs.CV20239 cited

Denoising Diffusion Models for Plug-and-Play Image Restoration

Yuanzhi Zhu, Kai Zhang, Jingyun Liang +4

Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser…

cs.LG20232 cited

Towards Adversarially Robust Continual Learning

Tao Bai, Chen Chen, Lingjuan Lyu +2

Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual…