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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| OSSGAN: Open-Set Semi-Supervised Image Generation |
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| Kai Katsumata, Duc Minh Vo, Hideki Nakayama |
| 2022 |
| arxiv 2204.14249 |
| Generative Convolution Layer for Image Generation | Seung Park, Yong-Goo Shin | 2021 | arxiv 2111.15171 |
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| On Data Scaling in Masked Image Modeling | Zhenda Xie, Zheng Zhang, Yue Cao | 2022 | arxiv 2206.04664 |
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| Danish Fungi 2020 -- Not Just Another Image Recognition Dataset | Lukáš Picek, Milan Šulc, Jiří Matas | 2021 | arxiv 2103.10107 |
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| Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations | Jinyuan Jia, Binghui Wang, Xiaoyu Cao | 2020 | arxiv 2011.07633 |
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| Fast Adversarial Training with Adaptive Step Size | Zhichao Huang, Yanbo Fan, Chen Liu | 2022 | arxiv 2206.02417 |
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| Robust fine-tuning of zero-shot models | Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim | 2021 | arxiv 2109.01903 |
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| SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling | Ho Man Kwan, Shenghui Song | 2022 | arxiv 2207.11511 |
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| Attention Distillation: self-supervised vision transformer students need more guidance | Kai Wang, Fei Yang, Joost van de Weijer | 2022 | arxiv 2210.00944 |
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| MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization | Mingkai Jia, Wei Yin, Xiaotao Hu | 2025 | arxiv 2507.07997 |
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