{"total":23,"rows":[{"id":"2106.12423","title":"Alias-Free Generative Adversarial Networks","created":"2021-06-23","cited_by_count":1184,"area":null},{"id":"2105.07926","title":"Towards Robust Vision Transformer","created":"2021-05-17","cited_by_count":137,"area":null},{"id":"2110.13771","title":"AugMax: Adversarial Composition of Random Augmentations for Robust Training","created":"2021-10-26","cited_by_count":90,"area":null},{"id":"2302.09462","title":"MedViT: A Robust Vision Transformer for Generalized Medical Image Classification","created":"2023-02-19","cited_by_count":89,"area":null},{"id":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37,"area":null},{"id":"2106.04823","title":"Taxonomy of Machine Learning Safety: A Survey and Primer","created":"2021-06-09","cited_by_count":25,"area":null},{"id":"2110.15156","title":"Blending Anti-Aliasing into Vision Transformer","created":"2021-10-28","cited_by_count":16,"area":null},{"id":"2105.12639","title":"Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness","created":"2021-05-26","cited_by_count":14,"area":null},{"id":"2205.04533","title":"How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?","created":"2022-05-09","cited_by_count":11,"area":null},{"id":"2303.08085","title":"Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations","created":"2023-03-14","cited_by_count":11,"area":null},{"id":"2102.07757","title":"How Convolutional Neural Networks Deal with Aliasing","created":"2021-02-15","cited_by_count":10,"area":null},{"id":"2107.12052","title":"Using Synthetic Corruptions to Measure Robustness to Natural Distribution Shifts","created":"2021-07-26","cited_by_count":7,"area":null},{"id":"2102.08868","title":"Bridging the Gap Between Adversarial Robustness and Optimization Bias","created":"2021-02-17","cited_by_count":5,"area":null},{"id":"2107.00623","title":"Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks","created":"2021-07-01","cited_by_count":5,"area":null},{"id":"2110.10536","title":"Improving Model Generalization by Agreement of Learned Representations from Data Augmentation","created":"2021-10-20","cited_by_count":5,"area":null},{"id":"2110.10645","title":"Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs","created":"2021-10-20","cited_by_count":5,"area":null},{"id":"2304.10622","title":"Enhancing object detection robustness: A synthetic and natural perturbation approach","created":"2023-04-20","cited_by_count":3,"area":null},{"id":"2209.11740","title":"On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks","created":"2022-09-19","cited_by_count":2,"area":null},{"id":"2310.14221","title":"The Importance of Anti-Aliasing in Tiny Object Detection","created":"2023-10-22","cited_by_count":2,"area":null},{"id":"2408.04077","title":"PushPull-Net: Inhibition-driven ResNet robust to image corruptions","created":"2024-08-07","cited_by_count":2,"area":null},{"id":"2510.22673","title":"Alias-Free ViT: Fractional Shift Invariance via Linear Attention","created":"2025-10-26","cited_by_count":2,"area":null},{"id":"2502.01490","title":"MoireDB: Formula-generated Interference-fringe Image Dataset","created":"2025-02-03","cited_by_count":0,"area":null},{"id":"2505.01656","title":"WaveInst: A Frequency-Domain Enhanced Network for Fine-Grained Thin Tree Trunk Extraction in Forest Scenes","created":"2025-05-03","cited_by_count":0,"area":null}],"next_cursor":null}