In this study, we present a Fast Knowledge Distillation (FKD) framework that replicates the distillation training phase and generates soft labels using the multi-crop KD approach, meanwhile training faster than ReLabel since no post-processes such as RoI align and softmax operations are used. When...
While Knowledge Distillation (KD) has been recognized as a useful tool in many visual tasks, such as supervised classification and self-supervised representation learning, the main drawback of a vanilla KD framework is its mechanism, which consumes the majority of the computational overhead on forwa...
FKD (@ECCV'22): A Fast Knowledge Distillation Framework for Visual Recognition Bibtex @inproceedings{shen2023ferkd, title={FerKD: Surgical Label Adaptation for Efficient Distillation}, author={Zhiqiang Shen}, booktitle={ICCV}, year={2023} } @inproceedings{shen2021afast, title={A Fast Knowledge...
Kim, A gift from knowledge distillation: Fast optimization, network minimization and transfer learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4133–4141. Google Scholar [12] Kim J., Park S., Kwak N. Paraphrasing complex network: Network ...
损失函数 其中Wt和Ws即为教师和学生网络的权重, 分别代表教师和学生网络的第i个FSP矩阵(此时默认学生和教师网络拥有同样数量和大小的FSP矩阵),N代表总的数据量, 表示损失函数中每一项的权重。在实际使用时通常认为每个损失项具有同样的重要性,因此使用相同的权重。
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A Gift from Knowledge Distillation:Fast Optiization,Network Minimization and Transfer Learning,程序员大本营,技术文章内容聚合第一站。
2020. A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data. In SIGIR. 831–840. ^abJiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, and Keping Yang. 2021. AutoDebias: Learning to Debias for Recommendation. In SIGIR ’21...
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