74 citations · 149 across the 8 of their papers we have counts for
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cs.CV2021★ 6 cited
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?
Zhisheng Xiao, Qing Yan, Yali Amit
Unsupervised outlier detection, which predicts if a test sample is an outlier or not using only the information from unlabelled inlier data, is an important but challenging task. R…
cs.CV2021★ 9 cited
Diversifying Sample Generation for Accurate Data-Free Quantization
Xiangguo Zhang, Haotong Qin, Yifu Ding +6
Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical…
cs.LG2021
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss
Zhisheng Xiao, Qing Yan, Yali Amit
Maximum likelihood estimation is widely used in training Energy-based models (EBMs). Training requires samples from an unnormalized distribution, which is usually intractable, and…