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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Abrar H. Abdulnabi, Bing Shuai, Zhen Zuo |
| 2018 |
| arxiv 1803.04687 |
| Single-Shot Object Detection with Enriched Semantics | Zhishuai Zhang, Siyuan Qiao, Cihang Xie | 2017 | arxiv 1712.00433 |
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| CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks | Andrew Spek, Thanuja Dharmasiri, Tom Drummond | 2018 | arxiv 1807.08931 |
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| Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes | Pengyuan Lyu, Minghui Liao, Cong Yao | 2018 | arxiv 1807.02242 |
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| Self-Erasing Network for Integral Object Attention | Qibin Hou, Peng-Tao Jiang, Yunchao Wei | 2018 | arxiv 1810.09821 |
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| Net2Brain: A Toolbox to compare artificial vision models with human brain responses | Domenic Bersch, Kshitij Dwivedi, Martina Vilas | 2022 | arxiv 2208.09677 |
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| Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks | Chaoyu Liu, Zhonghua Qiao, Chao Li | 2023 | arxiv 2307.07344 |
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| Geometric Deep Learning and Equivariant Neural Networks | Jan E. Gerken, Jimmy Aronsson, Oscar Carlsson | 2021 | arxiv 2105.13926 |
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| Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy | Luca Clissa, Antonio Macaluso, Roberto Morelli | 2023 | arxiv 2307.14243 |
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| Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning | Shiyu Tang, Peijun Tang, Yanxiang Gong | 2021 | arxiv 2103.12371 |
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| DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation | Seunghun Lee, Sunghyun Cho, Sunghoon Im | 2021 | arxiv 2103.13447 |
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