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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Hospital Data | Haresh Rengaraj Rajamohan, Yuxuan Chen, Kyunghyun Cho | 2026 | arxiv 2603.24903 |
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| Do Vision Models Encode Object-Level Semantic Relatedness? A Cognitive Psychology-Inspired Benchmark | Hansang Lee, Haeil Lee, Junmo Kim | 2017 | arxiv 1709.03806 |
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| How much human-like visual experience do current self-supervised learning algorithms need in order to achieve human-level object recognition? | A. Emin Orhan | 2021 | arxiv 2109.11523 |
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| Masked Contrastive Representation Learning | Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami | 2022 | arxiv 2211.06012 |
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| Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models | Kaiyue Wen, Jiaye Teng, Jingzhao Zhang | 2022 | arxiv 2206.00501 |
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| Taxonomic Class Incremental Learning | Yuzhao Chen, Zonghuan Li, Zhiyuan Hu | 2023 | arxiv 2304.05547 |
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| Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language | Alexei Baevski, Arun Babu, Wei-Ning Hsu | 2022 | arxiv 2212.07525 |
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| Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer Learning | Dina B. Efremova, Mangalam Sankupellay, Dmitry A. Konovalov | 2019 | arxiv 1909.07526 |
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| Automatic Pruning for Quantized Neural Networks | Luis Guerra, Bohan Zhuang, Ian Reid | 2020 | arxiv 2002.00523 |
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| A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark | Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov | 2019 | arxiv 1910.04867 |
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