← Learning Types
Deep Learning › Learning Types ›

Adversarial Learning

2063 directly classified papers

Papers per year

Papers

Are “Undocumented Workers” the Same as “Illegal Aliens”? Disentangling Denotation and Connotation in Vector Spaces EMNLP 2020
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training EMNLP 2020
Robust Adversarial Objects against Deep Learning Models AAAI 2020
Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis AAAI 2020
Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval AAAI 2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks NIPS 2020
Adversarial Training is a Form of Data-dependent Operator Norm Regularization NIPS 2020
Efficient Generation of Structured Objects with Constrained Adversarial Networks NIPS 2020
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples NIPS 2020
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness NIPS 2020
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models NIPS 2020
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling NIPS 2020
Few-Cost Salient Object Detection with Adversarial-Paced Learning NIPS 2020
MetaPoison: Practical General-purpose Clean-label Data Poisoning NIPS 2020
On the Trade-off between Adversarial and Backdoor Robustness NIPS 2020
Adversarial Deep Network Embedding for Cross-Network Node Classification AAAI 2020
A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories AAAI 2020
Suspicion-Free Adversarial Attacks on Clustering Algorithms AAAI 2020
Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning AAAI 2020
Adversarially Robust Distillation AAAI 2020
On-Line Adaptative Curriculum Learning for GANs AAAI 2019
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions CVPR 2019
Learning to Generate Synthetic Data via Compositing CVPR 2019
Feature Denoising for Improving Adversarial Robustness CVPR 2019
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack CVPR 2019