Adversarial Learning

Collaborative Sampling in Generative Adversarial Networks AAAI 2020 ColorFool: Semantic Adversarial Colorization CVPR 2020 CONAN: Complementary Pattern Augmentation for Rare Disease Detection AAAI 2020 ConjNLI: Natural Language Inference Over Conjunctive Sentences EMNLP 2020 Consistency Regularization for Certified Robustness of Smoothed Classifiers NeurIPS 2020 Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition Interspeech 2020 Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation EMNLP 2020 Conv-TasSAN: Separative Adversarial Network Based on Conv-TasNet Interspeech 2020 Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation ACL 2020 Cross-lingual Spoken Language Understanding with Regularized Representation Alignment EMNLP 2020 CrossNet: Latent Cross-Consistency for Unpaired Image Translation WACV 2020 Debiasing knowledge graph embeddings EMNLP 2020 Deep 3D Portrait From a Single Image CVPR 2020 Deep Adversarial Decomposition: A Unified Framework for Separating Superimposed Images CVPR 2020 Deep Domain-Adversarial Image Generation for Domain Generalisation AAAI 2020 DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training ICML 2020 Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval AAAI 2020 Defending Against Model Stealing Attacks With Adaptive Misinformation CVPR 2020 Defending Against Universal Attacks Through Selective Feature Regeneration CVPR 2020 Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack CVPR 2020 DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets AAAI 2020 Demoting Racial Bias in Hate Speech Detection ACL 2020 Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors CVPR 2020 Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks EMNLP 2020 Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation CVPR 2020