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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| 2019 |
| arxiv 1910.12976 |
| Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks | Muhammad Sabih, Frank Hannig, Juergen Teich | 2020 | arxiv 2008.09072 |
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| Attention Augmented Convolutional Networks | Irwan Bello, Barret Zoph, Ashish Vaswani | 2019 | arxiv 1904.09925 |
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| Assessing The Importance Of Colours For CNNs In Object Recognition | Aditya Singh, Alessandro Bay, Andrea Mirabile | 2020 | arxiv 2012.06917 |
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| Learning Data Augmentation Strategies for Object Detection | Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi | 2019 | arxiv 1906.11172 |
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| Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems | Mark D. McDonnell, Hesham Mostafa, Runchun Wang | 2019 | arxiv 1907.06916 |
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| A study on the Interpretability of Neural Retrieval Models using DeepSHAP | Zeon Trevor Fernando, Jaspreet Singh, Avishek Anand | 2019 | arxiv 1907.06484 |
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| Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection | Junran Peng, Ming Sun, Zhaoxiang Zhang | 2019 | arxiv 1909.02293 |
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| A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging | Riqiang Gao, Thomas Li, Yucheng Tang | 2022 | arxiv 2206.08833 |
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| Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative Learning | Bo Li, Yongqiang Yao, Jingru Tan | 2022 | arxiv 2210.05568 |
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