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cs.LG2023★ 4 cited
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Zhihang Yuan, Jiawei Liu, Jiaxiang Wu +6
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures. Despite its e…
cs.LG2021
Noise Modulation: Let Your Model Interpret Itself
Haoyang Li, Xinggang Wang
Given the great success of Deep Neural Networks(DNNs) and the black-box nature of it,the interpretability of these models becomes an important issue.The majority of previous resear…
cs.LG2019★ 8 cited
Reducing the dilution: An analysis of the information sensitiveness of capsule network with a practical improvement method
Zonglin Yang, Xinggang Wang
Capsule network has shown various advantages over convolutional neural network (CNN). It keeps more precise spatial information than CNN and uses equivariance instead of invariance…