Adversarial Learning

Improving Adversarial Robustness via Guided Complement Entropy ICCV 2019 Improving Adversarial Robustness via Promoting Ensemble Diversity ICML 2019 Improving Black-box Adversarial Attacks with a Transfer-based Prior NeurIPS 2019 Improving Domain Adaptation Translation with Domain Invariant and Specific Information NAACL 2019 Improving Hypernymy Prediction via Taxonomy Enhanced Adversarial Learning AAAI 2019 Improving Image Captioning with Conditional Generative Adversarial Nets AAAI 2019 Improving Mongolian-Chinese Neural Machine Translation with Morphological Noise ACL 2019 Improving Neural Language Modeling via Adversarial Training ICML 2019 Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss IJCAI 2019 Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior EMNLP 2019 Incremental Learning Using Conditional Adversarial Networks ICCV 2019 Inherent Tradeoffs in Learning Fair Representations NeurIPS 2019 Inserting Videos Into Videos CVPR 2019 Insufficient Data Can Also Rock! Learning to Converse Using Smaller Data with Augmentation AAAI 2019 Interpretation of Neural Networks Is Fragile AAAI 2019 Interpreting Adversarially Trained Convolutional Neural Networks ICML 2019 Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness NeurIPS 2019 Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications NAACL 2019 Jointly Adversarial Enhancement Training for Robust End-to-End Speech Recognition Interspeech 2019 Knockoff Nets: Stealing Functionality of Black-Box Models CVPR 2019 Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications CVPR 2019 Knowledge Distillation with Adversarial Samples Supporting Decision Boundary AAAI 2019 Label-Noise Robust Generative Adversarial Networks CVPR 2019 LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup ICCV 2019 Large Scale Adversarial Representation Learning NeurIPS 2019