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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI | Erico Tjoa, Hong Jing Khok, Tushar Chouhan | 2021 | arxiv 2201.00009 |
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| Improving Performance of Semi-Supervised Learning by Adversarial Attacks | Dongyoon Yang, Kunwoong Kim, Yongdai Kim | 2023 | arxiv 2308.04018 |
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| Fully Connected Deep Structured Networks | Alexander G. Schwing, Raquel Urtasun | 2015 | arxiv 1503.02351 |
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| Revisiting Winner Take All (WTA) Hashing for Sparse Datasets | Beidi Chen, Anshumali Shrivastava | 2016 | arxiv 1612.01834 |
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| Training Sparse Neural Networks | Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu | 2016 | arxiv 1611.06694 |
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| Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network | Sultan Imangaliyev, Monique H. van der Veen, Catherine M. C. Volgenant | 2017 | arxiv 1705.09193 |
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| Ensemble Of Deep Neural Networks For Acoustic Scene Classification | Venkatesh Duppada, Sushant Hiray | 2017 | arxiv 1708.05826 |
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| Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat | Erdong Hu, Yuxin Tang, Anastasios Kyrillidis | 2023 | arxiv 2309.03237 |
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| Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization | Michael Kohler, Adam Krzyzak, Alisha Sänger | 2024 | arxiv 2404.07128 |
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| Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation | Askar Tsyganov, Evgeny Frolov, Sergey Samsonov | 2025 | arxiv 2508.04444 |
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