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20182026
most citedXFake: Explainable Fake News Detector with Visualizations

103 citations · 235 across the 22 of their papers we have counts for

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Showing 2019Show all

9 papers · 1 filter

cs.CV2019

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…

cs.CV2019

Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder

Mengnan Du, Shiva Pentyala, Yuening Li +1

With advancements of deep learning techniques, it is now possible to generate super-realistic images and videos, i.e., deepfakes. These deepfakes could reach mass audience and resu…

cs.LG2019

Evaluating Explanation Without Ground Truth in Interpretable Machine Learning

Fan Yang, Mengnan Du, Xia Hu

Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are signif…

cs.LG2019

Learning Credible Deep Neural Networks with Rationale Regularization

Mengnan Du, Ninghao Liu, Fan Yang +1

Recent explainability related studies have shown that state-of-the-art DNNs do not always adopt correct evidences to make decisions. It not only hampers their generalization but al…

cs.LG2019

Deep Structured Cross-Modal Anomaly Detection

Yuening Li, Ninghao Liu, Jundong Li +2

Anomaly detection is a fundamental problem in data mining field with many real-world applications. A vast majority of existing anomaly detection methods predominately focused on da…

cs.LG2019

Fairness in Deep Learning: A Computational Perspective

Mengnan Du, Fan Yang, Na Zou +1

Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. However, deep learning models might exhibit algorithmic discrimina…