IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization

Learning to synthesize data has emerged as a promising direction in zero-shot quantization (ZSQ), which represents neural networks by low-bit integer without accessing any of the real data.

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Cite this as

Yunshan Zhong, Mingbao Lin, Gongrui Nan, Jianzhuang Liu, Baochang Zhang, Yonghong Tian, Rongrong Ji (2024). Dataset: IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization. https://doi.org/10.57702/m63psky5

DOI retrieved: December 2, 2024

Additional Info

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Created December 2, 2024
Last update December 2, 2024
Author Yunshan Zhong
More Authors
Mingbao Lin
Gongrui Nan
Jianzhuang Liu
Baochang Zhang
Yonghong Tian
Rongrong Ji
Homepage https://github.com/zysxmu/IntraQ