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个人简介
Recently, many problems have been solved with deep learning using massive computational sources, often referred to as red AI. On the other hand, I am interested in green deep learning that considers energy usage and carbon emissions during model training and inference. Among the various compression technologies to obtain lightweight models, my previous works are mainly focused on efficient inference approaches such as network quantization and pruning. Specifically, several projects are about test-time adaptation of computational resources based on the sensitivity of the input image to compression (i.e., the less sensitive the input is, the fewer computational resources that are allocated). Although my latest projects are on compressing models for low-level image restoration problems, my research goal is to compress any deep learning model with massive computations.
研究兴趣
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CVPR 2024 (2024)
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IEEE SIGNAL PROCESSING LETTERS (2024): 91-95
IEEE Conference on Computer Vision and Pattern Recognitionno. 1 (2022): 17652-17661
arXiv (Cornell University) (2021)
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D-Core
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