ADEQ: Adaptive Diversity Enhancement for Zero-Shot Quantization

Author:

Chen Xinrui,Yan Renao,Cheng Junru,Wang Yizhi,Fu Yuqiu,Chen Yi,Guan Tian,He Yonghong

Publisher

Springer Nature Singapore

Reference30 articles.

1. Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704–2713 (2018)

2. Nagel, M., Fournarakis, M., Amjad, R.A., Bondarenko, Y., Baalen, M.V., Blankevoort, T.: A white paper on neural network quantization (2021). arXiv preprint arXiv:2106.08295

3. Krishnamoorthi, R.: Quantizing deep convolutional networks for efficient inference: A whitepaper (2018). arXiv preprint arXiv:1806.08342

4. Kim, D., Lee, J., Ham, B.: Distance-aware quantization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5271–5280 (2021)

5. Nahshan, Y., et al.: Loss aware post-training quantization. Mach. Learn. 110(11–12), 3245–3262 (2021)

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