{"total":88,"rows":[{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579,"area":null},{"id":"2112.05135","title":"PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures","created":"2021-12-09","cited_by_count":131,"area":null},{"id":"2203.01441","title":"3D Common Corruptions and Data Augmentation","created":"2022-03-02","cited_by_count":119,"area":null},{"id":"2211.13081","title":"Robust Mean Teacher for Continual and Gradual Test-Time Adaptation","created":"2022-11-23","cited_by_count":104,"area":null},{"id":"2506.06659","title":"DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning","created":"2025-06-07","cited_by_count":94,"area":null},{"id":"2201.12296","title":"Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions","created":"2022-01-28","cited_by_count":80,"area":null},{"id":"2407.05848","title":"Wavelet Convolutions for Large Receptive Fields","created":"2024-07-08","cited_by_count":74,"area":null},{"id":"2103.02325","title":"On the effectiveness of adversarial training against common corruptions","created":"2021-03-03","cited_by_count":71,"area":null},{"id":"2207.03442","title":"Back to the Source: Diffusion-Driven Test-Time Adaptation","created":"2022-07-07","cited_by_count":71,"area":null},{"id":"2106.14999","title":"Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation","created":"2021-06-28","cited_by_count":68,"area":null},{"id":"2306.00650","title":"Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction","created":"2023-06-01","cited_by_count":65,"area":null},{"id":"2211.01866","title":"ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations","created":"2022-11-03","cited_by_count":44,"area":null},{"id":"2402.07410","title":"A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)","created":"2024-02-12","cited_by_count":44,"area":null},{"id":"2112.13547","title":"PRIME: A few primitives can boost robustness to common corruptions","created":"2021-12-27","cited_by_count":40,"area":null},{"id":"2310.09562","title":"Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?","created":"2023-10-14","cited_by_count":35,"area":null},{"id":"2107.13389","title":"SimROD: A Simple Adaptation Method for Robust Object Detection","created":"2021-07-28","cited_by_count":27,"area":null},{"id":"2309.01439","title":"Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN","created":"2023-09-04","cited_by_count":25,"area":null},{"id":"2405.05012","title":"The Entropy Enigma: Success and Failure of Entropy Minimization","created":"2024-05-08","cited_by_count":24,"area":null},{"id":"2207.07065","title":"On the Strong Correlation Between Model Invariance and Generalization","created":"2022-07-14","cited_by_count":23,"area":null},{"id":"2408.07511","title":"Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach","created":"2024-08-14","cited_by_count":23,"area":null},{"id":"2302.01094","title":"Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy Estimation","created":"2023-02-02","cited_by_count":22,"area":null},{"id":"2305.06024","title":"A Survey on the Robustness of Computer Vision Models against Common Corruptions","created":"2023-05-10","cited_by_count":21,"area":null},{"id":"2112.03740","title":"Dilated convolution with learnable spacings","created":"2021-12-07","cited_by_count":20,"area":null},{"id":"2404.01509","title":"Can Biases in ImageNet Models Explain Generalization?","created":"2024-04-01","cited_by_count":20,"area":null},{"id":"2403.01944","title":"Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification","created":"2024-03-04","cited_by_count":18,"area":null},{"id":"2112.00659","title":"Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines","created":"2021-12-01","cited_by_count":17,"area":null},{"id":"2302.03629","title":"Ethical Considerations for Responsible Data Curation","created":"2023-02-07","cited_by_count":17,"area":null},{"id":"2301.06013","title":"Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning","created":"2023-01-15","cited_by_count":14,"area":null},{"id":"2310.04780","title":"IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers","created":"2023-10-07","cited_by_count":14,"area":null},{"id":"2403.12033","title":"HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation","created":"2024-03-18","cited_by_count":14,"area":null},{"id":"2507.01831","title":"Out-of-Distribution Detection Methods Answer the Wrong Questions","created":"2025-07-02","cited_by_count":14,"area":null},{"id":"2208.07736","title":"Introducing Intermediate Domains for Effective Self-Training during Test-Time","created":"2022-08-16","cited_by_count":12,"area":null},{"id":"2307.11823","title":"HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness","created":"2023-07-21","cited_by_count":12,"area":null},{"id":"2310.04414","title":"CIFAR-10-Warehouse: Broad and More Realistic Testbeds in Model Generalization Analysis","created":"2023-10-06","cited_by_count":12,"area":null},{"id":"2303.13251","title":"A Bag-of-Prototypes Representation for Dataset-Level Applications","created":"2023-03-23","cited_by_count":11,"area":null},{"id":"2410.03030","title":"Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness","created":"2024-10-03","cited_by_count":11,"area":null},{"id":"2303.09767","title":"It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness","created":"2023-03-17","cited_by_count":9,"area":null},{"id":"2307.10586","title":"A Holistic Assessment of the Reliability of Machine Learning Systems","created":"2023-07-20","cited_by_count":9,"area":null},{"id":"2301.12993","title":"Benchmarking Robustness to Adversarial Image Obfuscations","created":"2023-01-30","cited_by_count":8,"area":null},{"id":"2405.18979","title":"MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts","created":"2024-05-29","cited_by_count":8,"area":null},{"id":"2406.16540","title":"Improving robustness to corruptions with multiplicative weight perturbations","created":"2024-06-24","cited_by_count":7,"area":null},{"id":"2402.05960","title":"Phase-driven Domain Generalizable Learning for Nonstationary Time Series","created":"2024-02-05","cited_by_count":5,"area":null},{"id":"2503.03519","title":"Do ImageNet-trained models learn shortcuts? The impact of frequency shortcuts on generalization","created":"2025-03-05","cited_by_count":5,"area":null},{"id":"2212.04681","title":"Dynamic Test-Time Augmentation via Differentiable Functions","created":"2022-12-09","cited_by_count":4,"area":null},{"id":"2305.05400","title":"Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions","created":"2023-05-09","cited_by_count":4,"area":null},{"id":"2401.00989","title":"Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation","created":"2024-01-02","cited_by_count":4,"area":null},{"id":"2311.12800","title":"Understanding Data Augmentation from a Robustness Perspective","created":"2023-09-07","cited_by_count":3,"area":null},{"id":"2312.10088","title":"On Robustness to Missing Video for Audiovisual Speech Recognition","created":"2023-12-13","cited_by_count":3,"area":null},{"id":"2406.16935","title":"Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex","created":"2024-06-16","cited_by_count":3,"area":null},{"id":"2504.12606","title":"Robo-SGG: Exploiting Layout-Oriented Normalization and Restitution Can Improve Robust Scene Graph Generation","created":"2025-04-17","cited_by_count":3,"area":null},{"id":"2506.16950","title":"LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models","created":"2025-06-20","cited_by_count":3,"area":null},{"id":"2511.14440","title":"Learning to See Through a Baby's Eyes: Early Visual Diets Enable Robust Visual Intelligence in Humans and Machines","created":"2025-11-18","cited_by_count":3,"area":null},{"id":"2309.13150","title":"Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations","created":"2023-09-22","cited_by_count":2,"area":null},{"id":"2402.18402","title":"A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation","created":"2024-02-28","cited_by_count":2,"area":null},{"id":"2402.19401","title":"Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance","created":"2024-02-29","cited_by_count":2,"area":null},{"id":"2206.07918","title":"\"Understanding Robustness Lottery\": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches","created":"2022-06-16","cited_by_count":1,"area":null},{"id":"2303.09283","title":"Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity","created":"2023-03-15","cited_by_count":1,"area":null},{"id":"2306.07178","title":"Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions","created":"2023-06-12","cited_by_count":1,"area":null},{"id":"2310.08139","title":"DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection","created":"2023-10-12","cited_by_count":1,"area":null},{"id":"2406.00529","title":"On the Use of Anchoring for Training Vision Models","created":"2024-06-01","cited_by_count":1,"area":null},{"id":"2406.01425","title":"Adaptive Sensitivity Analysis for Robust Augmentation against Natural Corruptions in Image Segmentation","created":"2024-06-03","cited_by_count":1,"area":null},{"id":"2408.01977","title":"Label Augmentation for Neural Networks Robustness","created":"2024-08-04","cited_by_count":1,"area":null},{"id":"2505.23173","title":"Pseudo Multi-Source Domain Generalization: Bridging the Gap Between Single and Multi-Source Domain Generalization","created":"2025-05-29","cited_by_count":1,"area":null},{"id":"2505.24592","title":"A Flat Minima Perspective on Understanding Augmentations and Model Robustness","created":"2025-05-30","cited_by_count":1,"area":null},{"id":"2506.06782","title":"Feature-Based Instance Neighbor Discovery: Advanced Stable Test-Time Adaptation in Dynamic World","created":"2025-06-07","cited_by_count":1,"area":null},{"id":"2604.04780","title":"CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models","created":"2026-04-06","cited_by_count":1,"area":null},{"id":"2605.15906","title":"A Causally Grounded Taxonomy for Image Degradation Robustness Evaluation","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2608.18900","title":"TestifAI: Tomography-Based Testing for Deep Learning Systems","created":"2026-08-19","cited_by_count":1,"area":null},{"id":"2212.04059","title":"MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation","created":"2022-12-08","cited_by_count":0,"area":null},{"id":"2303.02400","title":"Fine-Grained ImageNet Classification in the Wild","created":"2023-03-04","cited_by_count":0,"area":null},{"id":"2308.12661","title":"Don't Look into the Sun: Adversarial Solarization Attacks on Image Classifiers","created":"2023-08-24","cited_by_count":0,"area":null},{"id":"2404.10314","title":"Awareness of uncertainty in classification using a multivariate model and multi-views","created":"2024-04-16","cited_by_count":0,"area":null},{"id":"2409.18419","title":"Robust Network Learning via Inverse Scale Variational Sparsification","created":"2024-09-27","cited_by_count":0,"area":null},{"id":"2410.07691","title":"Growing Efficient Accurate and Robust Neural Networks on the Edge","created":"2024-10-10","cited_by_count":0,"area":null},{"id":"2501.04861","title":"LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep Learning","created":"2025-01-08","cited_by_count":0,"area":null},{"id":"2501.18098","title":"Disentangling Safe and Unsafe Corruptions via Anisotropy and Locality","created":"2025-01-30","cited_by_count":0,"area":null},{"id":"2508.17381","title":"DART: A Server-side Plug-in for Resource-efficient Robust Federated Learning","created":"2025-08-24","cited_by_count":0,"area":null},{"id":"2510.26833","title":"VISAT: Benchmarking Adversarial and Distribution Shift Robustness in Traffic Sign Recognition with Visual Attributes","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2511.12695","title":"A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning","created":"2025-11-16","cited_by_count":0,"area":null},{"id":"2512.12165","title":"Audio-Visual Camera Pose Estimation with Passive Scene Sounds and In-the-Wild Video","created":"2025-12-13","cited_by_count":0,"area":null},{"id":"2512.15675","title":"Stylized Synthetic Augmentation further improves Corruption Robustness","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2602.07320","title":"Incorruptible Neural Networks: Training Models that can Generalize to Large Internal Perturbations","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2603.03920","title":"BD-Merging: Bias-Aware Dynamic Model Merging with Evidence-Guided Contrastive Learning","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2606.18539","title":"TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2607.12881","title":"Inhibited Self-Attention: Sharpening Focus in Vision Transformers","created":"2026-07-14","cited_by_count":0,"area":null},{"id":"2608.04442","title":"Robustness Emerges Early in Training Dynamics, but Is Not Preserved","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.06712","title":"Suppress and Diversify: Refining Robust Pathways for Corruption Robustness","created":"2026-08-07","cited_by_count":0,"area":null},{"id":"2608.19882","title":"TESTNAV: Pareto-Guided Search for Compositional Robustness Testing","created":"2026-08-20","cited_by_count":0,"area":null}],"next_cursor":null}