← Learning Types
Machine Learning › Learning Types ›

Adversarial Learning

4854 directly classified papers

Papers per year

Papers

Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry ICCV 2019
Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks ICCV 2019
Find Me if You Can: Deep Software Clone Detection by Exploiting the Contest between the Plagiarist and the Detector AAAI 2019
On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method ICCV 2019
AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks AAAI 2019
Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets AAAI 2019
Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient AAAI 2019
Reinforcement Learning under Threats AAAI 2019
Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images ICCV 2019
Significance-Aware Information Bottleneck for Domain Adaptive Semantic Segmentation ICCV 2019
Multi-Adversarial Faster-RCNN for Unrestricted Object Detection ICCV 2019
Generative Multi-View Human Action Recognition ICCV 2019
Adversarial Representation Learning for Text-to-Image Matching ICCV 2019
Physical Adversarial Textures That Fool Visual Object Tracking ICCV 2019
Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition ICCV 2019
Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification ICCV 2019
SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation ICCV 2019
Towards Adversarially Robust Object Detection ICCV 2019
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation ICCV 2019
Robust Audio Adversarial Example for a Physical Attack IJCAI 2019
Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling ACL 2019
Large Scale Adversarial Representation Learning NIPS 2019
Detecting Overfitting via Adversarial Examples NIPS 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers NIPS 2019
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness NIPS 2019