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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| 2021 |
| arxiv 2108.04423 |
| eGAN: Unsupervised approach to class imbalance using transfer learning | Ademola Okerinde, Lior Shamir, William Hsu | 2021 | arxiv 2104.04162 |
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| Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths | Ximeng Sun, Rameswar Panda, Chun-Fu Chen | 2021 | arxiv 2103.01435 |
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| Just Noticeable Difference for Deep Machine Vision | Jian Jin, Xingxing Zhang, Xin Fu | 2021 | arxiv 2102.08168 |
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| Convolutional Xformers for Vision | Pranav Jeevan, Amit sethi | 2022 | arxiv 2201.10271 |
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| Unlabeled Data vs. Pre-trained Knowledge: Rethinking SSL in the Era of Large Models | Song-Lin Lv, Rui Zhu, Tong Wei | 2025 | arxiv 2505.13317 |
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| Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study | Raoof Naushad, Tarunpreet Kaur, Ebrahim Ghaderpour | 2021 | arxiv 2110.02580 |
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| Visualizing and Understanding Contrastive Learning | Fawaz Sammani, Boris Joukovsky, Nikos Deligiannis | 2022 | arxiv 2206.09753 |
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| Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms | Bo Liu, Liqiang Yu, Chang Che | 2023 | arxiv 2312.12872 |
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| ActiveDC: Distribution Calibration for Active Finetuning | Wenshuai Xu, Zhenghui Hu, Yu Lu | 2023 | arxiv 2311.07634 |
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| Convolutional Channel-wise Competitive Learning for the Forward-Forward Algorithm | Andreas Papachristodoulou, Christos Kyrkou, Stelios Timotheou | 2023 | arxiv 2312.12668 |
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