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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Songyang Zhang, Zeming Li, Shipeng Yan |
| 2021 |
| arxiv 2103.16370 |
| Locally orderless tensor networks for classifying two- and three-dimensional medical images | Raghavendra Selvan, Silas \Orting, Erik B Dam | 2020 | arxiv 2009.12280 |
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| Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm | Semih Kaya, Elif Vural | 2020 | arxiv 2006.02330 |
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| Matching Distributions via Optimal Transport for Semi-Supervised Learning | Fariborz Taherkhani, Hadi Kazemi, Ali Dabouei | 2020 | arxiv 2012.03790 |
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| Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training | Dominic Masters, Antoine Labatie, Zach Eaton-Rosen | 2021 | arxiv 2106.03640 |
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| Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density | Kuniaki Saito, Donghyun Kim, Piotr Teterwak | 2021 | arxiv 2108.10860 |
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| Supervised Video Summarization via Multiple Feature Sets with Parallel Attention | Junaid Ahmed Ghauri, Sherzod Hakimov, Ralph Ewerth | 2021 | arxiv 2104.11530 |
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| PatchDropout: Economizing Vision Transformers Using Patch Dropout | Yue Liu, Christos Matsoukas, Fredrik Strand | 2022 | arxiv 2208.07220 |
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| UPANets: Learning from the Universal Pixel Attention Networks | Ching-Hsun Tseng, Shin-Jye Lee, Jia-Nan Feng | 2021 | arxiv 2103.08640 |
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| How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers | Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai | 2021 | arxiv 2106.10270 |
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