Robustness

Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors NeurIPS 2022 Interpreting the Robustness of Neural NLP Models to Textual Perturbations ACL 2022 Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples EMNLP 2022 Machine Translation Robustness to Natural Asemantic Variation EMNLP 2022 MEMO: Test Time Robustness via Adaptation and Augmentation NeurIPS 2022 On Feature Learning in the Presence of Spurious Correlations NeurIPS 2022 On Length Divergence Bias in Textual Matching Models ACL 2022 On the Impact of Spurious Correlation for Out-of-Distribution Detection AAAI 2022 On the Robustness of Question Rewriting Systems to Questions of Varying Hardness ACL 2022 PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures CVPR 2022 Prediction Difference Regularization against Perturbation for Neural Machine Translation ACL 2022 Robust Image Forgery Detection Over Online Social Network Shared Images CVPR 2022 Robust Models are less Over-Confident NeurIPS 2022 Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions EMNLP 2022 The King Is Naked: On the Notion of Robustness for Natural Language Processing AAAI 2022 Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning EMNLP 2022 Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness NeurIPS 2022 Vision Transformers Are Robust Learners AAAI 2022 Your Out-of-Distribution Detection Method is Not Robust! NeurIPS 2022 Achieving Model Robustness through Discrete Adversarial Training EMNLP 2021 Adaptive Verifiable Training Using Pairwise Class Similarity AAAI 2021 A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition NeurIPS 2021 Evaluating the Robustness of Neural Language Models to Input Perturbations EMNLP 2021 Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis ACL 2021 Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption AAAI 2021