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In object detection, the detection backbone consumes more than half of the overall inference cost.
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Detnet: Design backbone for object detection
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Darts: Differentiable architecture search
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Fishnet: A versatile backbone for image, region, and pixel level prediction
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Nas-fpn: Learning scalable feature pyramid architecture for object detection
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Complexity of linear regions in deep networks
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Pruning neural networks without any data by iteratively conserving synaptic flow
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Exploring self-attention for image recognition
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ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
Chan, K. H. R., Yu, Y., You, C., Qi, H., Wright, J., and Ma, Y · 2021
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Darts-: Robustly stepping out of performance collapse without indicators
Chu, X., Wang, X., Zhang, B., Lu, S., Wei, X., and Yan, J · 2021
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Opanas: One-shot path aggregation network architecture search for object detection
Liang, T., Wang, Y., Tang, Z., Hu, G., and Ling, H · 2021
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Zen-nas: A zero-shot nas for high-performance deep image recognition
Lin, M., Wang, P., Sun, Z., Chen, H., Sun, X., Qian, Q., Li, H., and Jin, R · 2021
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Neural architecture search without training
Mellor, J., Turner, J., Storkey, A., and Crowley, E. J · 2021
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Scnet: Training inference sample consistency for instance segmentation
Vu, T., Kang, H., and Yoo, C. D · 2021
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Mobiledets: Searching for object detection architectures for mobile accelerators
Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., Kindermans, P.-J., Tan, M., Singh, V., and Chen, B · 2021
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Giraffedet: A heavy-neck paradigm for object detection
Jiang, Y., Tan, Z., Wang, J., Sun, X., Lin, M., and Li, H · 2022
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