Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification-Reference-Cited by-同舟云学术

Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification

Published:2023-10-26 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Xuan Yunyi¹^ORCID,Chen Weijie²^ORCID,Yang Shicai¹^ORCID,Xie Di¹^ORCID,Lin Luojun³^ORCID,Zhuang Yueting⁴^ORCID

Affiliation:

1. Hikvision Research Institute, Hangzhou, China

2. Zhejiang University & Hikvision Research Institute, Hangzhou, China

3. Fuzhou University, Fuzhou, China

4. Zhejiang University, Hangzhou, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3611911

Reference36 articles.

1. K. Binici , S. Aggarwal , N. T. Pham , K. Leman , and T. Mitra . 2022. Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay . In Proceedings of the AAAI Conference on Artificial Intelligence. K. Binici, S. Aggarwal, N. T. Pham, K. Leman, and T. Mitra. 2022. Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay. In Proceedings of the AAAI Conference on Artificial Intelligence.

2. ImageCLEF 2014: Overview and Analysis of the Results

3. Data-Free Learning of Student Networks

4. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey E . Hinton . 2020 a. A Simple Framework for Contrastive Learning of Visual Representations. ArXiv , Vol. abs/ 2002 .05709 (2020). Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020a. A Simple Framework for Contrastive Learning of Visual Representations. ArXiv, Vol. abs/2002.05709 (2020).

5. Unsupervised Image Classification for Deep Representation Learning