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- W4283703886 abstract "Knowledge Distillation (KD) has recently emerged as a popular method for compressing neural networks. In recent studies, generalized distillation methods that find parameters and architectures of student models at the same time have been proposed. Still, this search method requires a lot of computation to search for architectures and has the disadvantage of considering only convolutional blocks in their search space. This paper introduces a new algorithm, coined as Trust Region Aware architecture search to Distill knowledge Effectively (TRADE), that rapidly finds effective student architectures from several state-of-the-art architectures using trust region Bayesian optimization approach. Experimental results show our proposed TRADE algorithm consistently outperforms both the conventional NAS approach and pre-defined architecture under KD training." @default.
- W4283703886 created "2022-06-30" @default.
- W4283703886 creator A5023870894 @default.
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- W4283703886 date "2022-06-27" @default.
- W4283703886 modified "2023-10-15" @default.
- W4283703886 title "Revisiting Architecture-aware Knowledge Distillation: Smaller Models and Faster Search" @default.
- W4283703886 doi "https://doi.org/10.48550/arxiv.2206.13130" @default.
- W4283703886 hasPublicationYear "2022" @default.
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