CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation

Data-Free Knowledge Distillation (DFKD) enables the knowledge transfer from the given pre-trained teacher network to the target student model without access to the real training data. Existing DFKD methods focus primarily on improving image recognition performance on associated datasets, often negle...

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Vydané v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori:	Zhang, Zherui, Wang, Changwei, Xu, Rongtao, Xu, Wenhao, Xu, Shibiao, Zhang, Yu, Zhou, Jie, Guo, Li
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 22.06.2025
Predmet:	Contrastive learning Design automation Generators Image recognition Knowledge Distillation Knowledge engineering Knowledge transfer Representation learning Training Training data Transfer learning
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