95 citations · 136 across the 6 of their papers we have counts for
6 papers
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking
Ruixiang Tang, Qizhang Feng, Ninghao Liu +2
The huge supporting training data on the Internet has been a key factor in the success of deep learning models. However, this abundance of public-available data also raises concern…
Differentially Private Counterfactuals via Functional Mechanism
Fan Yang, Qizhang Feng, Kaixiong Zhou +2
Counterfactual, serving as one emerging type of model explanation, has attracted tons of attentions recently from both industry and academia. Different from the conventional featur…
Model-Based Counterfactual Synthesizer for Interpretation
Fan Yang, Sahan Suresh Alva, Jiahao Chen +1
Counterfactuals, serving as one of the emerging type of model interpretations, have recently received attention from both researchers and practitioners. Counterfactual explanations…
XDeep: An Interpretation Tool for Deep Neural Networks
Fan Yang, Zijian Zhang, Haofan Wang +2
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers. Overall, XDeep takes a trained deep neural network (DNN) as the in…
Contextual Local Explanation for Black Box Classifiers
Zijian Zhang, Fan Yang, Haofan Wang +1
We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE. CLE gives an faithful and interpretable explanation to the predi…
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Haofan Wang, Zifan Wang, Mengnan Du +5
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions. In this paper, w…