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Adversarial Learning

2063 directly classified papers

Papers per year

Papers

GreedyFool: Distortion-Aware Sparse Adversarial Attack NIPS 2020
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets NIPS 2020
Consistency Regularization for Certified Robustness of Smoothed Classifiers NIPS 2020
Feature Disentanglement to Aid Imaging Biomarker Characterization for Genetic Mutations MIDL 2020
Domain Generalization Using a Mixture of Multiple Latent Domains AAAI 2020
Interactive Dual Generative Adversarial Networks for Image Captioning AAAI 2020
Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks AAAI 2020
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging NIPS 2020
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks NIPS 2020
Adversarial Learning for Robust Deep Clustering NIPS 2020
Targeted Adversarial Perturbations for Monocular Depth Prediction NIPS 2020
Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text ACL 2020
Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation ACL 2020
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations ACL 2020
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis ACL 2020
GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples ACL 2020
Neural Topic Modeling with Bidirectional Adversarial Training ACL 2020
QEBA: Query-Efficient Boundary-Based Blackbox Attack CVPR 2020
When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks CVPR 2020
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder CVPR 2020
Robustness Guarantees for Deep Neural Networks on Videos CVPR 2020
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks NIPS 2020
Certified Defense to Image Transformations via Randomized Smoothing NIPS 2020
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free NIPS 2020
Bridging Maximum Likelihood and Adversarial Learning via α-Divergence AAAI 2020