← Learning Types
Deep Learning › Learning Types ›

Adversarial Learning

2063 directly classified papers

Papers per year

Papers

Sensitivity Analysis of Deep Neural Networks AAAI 2019
Smooth Deep Image Generator from Noises AAAI 2019
Interpretation of Neural Networks Is Fragile AAAI 2019
Towards Reliable Learning for High Stakes Applications AAAI 2019
AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks AAAI 2019
Learning from Web Data Using Adversarial Discriminative Neural Networks for Fine-Grained Classification AAAI 2019
Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks ICCV 2019
LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup ICCV 2019
Adversarial Machine Learning with Double Oracle IJCAI 2019
Large Scale Adversarial Representation Learning NIPS 2019
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder NIPS 2019
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness NIPS 2019
Improving Black-box Adversarial Attacks with a Transfer-based Prior NIPS 2019
Unsupervised Object Segmentation by Redrawing NIPS 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers NIPS 2019
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness NIPS 2019
Learning from Label Proportions with Generative Adversarial Networks NIPS 2019
RUBi: Reducing Unimodal Biases for Visual Question Answering NIPS 2019
Adversarial Training and Robustness for Multiple Perturbations NIPS 2019
Deep Leakage from Gradients NIPS 2019
DM2C: Deep Mixed-Modal Clustering NIPS 2019
Functional Adversarial Attacks NIPS 2019
Image Synthesis with a Single (Robust) Classifier NIPS 2019
On Relating Explanations and Adversarial Examples NIPS 2019
Adversarial training for free! NIPS 2019