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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Zhen Dong, Yizhao Gao, Qijing Huang |
| 2021 |
| arxiv 2104.12766 |
| Transformed CNNs: recasting pre-trained convolutional layers with self-attention | Stéphane d'Ascoli, Levent Sagun, Giulio Biroli | 2021 | arxiv 2106.05795 |
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| Transfer Learning for Segmentation Problems: Choose the Right Encoder and Skip the Decoder | Jonas Dippel, Matthias Lenga, Thomas Goerttler | 2022 | arxiv 2207.14508 |
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| Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification | Prakash Chandra Chhipa, Richa Upadhyay, Rajkumar Saini | 2022 | arxiv 2210.10633 |
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| ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data | Ruiqi Yang, Eric Modesitt | 2023 | arxiv 2308.00454 |
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| Towards Inadequately Pre-trained Models in Transfer Learning | Andong Deng, Xingjian Li, Di Hu | 2022 | arxiv 2203.04668 |
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| Accelerating Large Batch Training via Gradient Signal to Noise Ratio (GSNR) | Guo-qing Jiang, Jinlong Liu, Zixiang Ding | 2023 | arxiv 2309.13681 |
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| Evaluating Self-Supervised Learning via Risk Decomposition | Yann Dubois, Tatsunori Hashimoto, Percy Liang | 2023 | arxiv 2302.03068 |
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| Deep Epitomic Convolutional Neural Networks | George Papandreou | 2014 | arxiv 1406.2732 |
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| WEPSAM: Weakly Pre-Learnt Saliency Model | Avisek Lahiri, Sourya Roy, Anirban Santara | 2016 | arxiv 1605.01101 |
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