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Post-training quantization (PTQ) for vision transformers (ViTs) has received increasing attention from both academic and industrial communities due to its minimal data needs and high time efficiency.
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2024
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Y. Zhong, J. Hu, Y. Huang, Y. Zhang, and R. Ji, “Erq: Error reduction for post-training quantization of vision transformers,” in Proceedings of the International Conference on Machine Learning (ICML) , 2024
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A. Ramachandran, S. Kundu, and T. Krishna, “Clamp-vit: Contrastive data-free learning for adaptive post-training quantization of vits,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2024
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J. Moon, D. Kim, J. Cheon, and B. Ham, “Instance-aware group quantization for vision transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024, pp. 16 132–16 141
2024
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Y. Ma, H. Li, X. Zheng, F. Ling, X. Xiao, R. Wang, S. Wen, F. Chao, and R. Ji, “Outlier-aware slicing for post-training quantization in vision transformer,” in Proceedings of the International Conference on Machine Learning (ICML) , 2024
2024
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H. Lin, H. Xu, Y. Wu, J. Cui, Y. Zhang, L. Mou, L. Song, Z. Sun, and Y. Wei, “Duquant: Distributing outliers via dual transformation makes stronger quantized llms,” in Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) , 2024
2024
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W. Shao, M. Chen, Z. Zhang, P. Xu, L. Zhao, Z. Li, K. Zhang, P. Gao, Y. Qiao, and P. Luo, “Omniquant: Omnidirectionally calibrated quantization for large language models,” in The Eleventh International Conference on Learning Representations (ICLR) , 2024
2024
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Z. Wu, J. Chen, H. Zhong, D. Huang, and Y. Wang, “Adalog: Post-training quantization for vision transformers with adaptive logarithm quantizer,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2024
2024
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