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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| 2023 |
| arxiv 2303.08983 |
| Natural Adversarial Examples | Dan Hendrycks, Kevin Zhao, Steven Basart | 2019 | arxiv 1907.07174 |
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| Shape-Texture Debiased Neural Network Training | Yingwei Li, Qihang Yu, Mingxing Tan | 2020 | arxiv 2010.05981 |
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| A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation | Zefeng Li, Evan Shelhamer | 2026 | arxiv 2606.01973 |
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| Diffusion Models Beat GANs on Image Synthesis | Prafulla Dhariwal, Alex Nichol | 2021 | arxiv 2105.05233 |
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| Can we Adopt Self-supervised Pretraining for Chest X-Rays? | Arsh Verma, Makarand Tapaswi | 2022 | arxiv 2211.12931 |
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| A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis | Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Ruibin Feng | 2021 | arxiv 2108.05930 |
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| Rethinking Natural Adversarial Examples for Classification Models | Xiao Li, Jianmin Li, Ting Dai | 2021 | arxiv 2102.11731 |
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| Pyramid Adversarial Training Improves ViT Performance | Charles Herrmann, Kyle Sargent, Lu Jiang | 2021 | arxiv 2111.15121 |
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| Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend | McKell Woodland, Austin Castelo, Mais Al Taie | 2023 | arxiv 2311.13717 |
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| Fixing the train-test resolution discrepancy: FixEfficientNet | Hugo Touvron, Andrea Vedaldi, Matthijs Douze | 2020 | arxiv 2003.08237 |
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