{"total":80,"rows":[{"id":"2103.03097","title":"Generalizing to Unseen Domains: A Survey on Domain Generalization","created":"2021-03-02","cited_by_count":766,"area":null},{"id":"2103.02503","title":"Domain Generalization: A Survey","created":"2021-03-03","cited_by_count":764,"area":null},{"id":"2203.17274","title":"Exploring Visual Prompts for Adapting Large-Scale Models","created":"2022-03-31","cited_by_count":274,"area":null},{"id":"2107.04649","title":"Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization","created":"2021-07-09","cited_by_count":249,"area":null},{"id":"2311.18803","title":"BioCLIP: A Vision Foundation Model for the Tree of Life","created":"2023-11-30","cited_by_count":161,"area":null},{"id":"2212.00638","title":"Finetune like you pretrain: Improved finetuning of zero-shot vision models","created":"2022-12-01","cited_by_count":150,"area":null},{"id":"2401.08541","title":"Scalable Pre-training of Large Autoregressive Image Models","created":"2024-01-16","cited_by_count":115,"area":null},{"id":"2206.13089","title":"Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift","created":"2022-06-27","cited_by_count":94,"area":null},{"id":"2112.05090","title":"Extending the WILDS Benchmark for Unsupervised Adaptation","created":"2021-12-09","cited_by_count":86,"area":null},{"id":"2202.08360","title":"Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision","created":"2022-02-16","cited_by_count":80,"area":null},{"id":"2105.03494","title":"The iWildCam 2021 Competition Dataset","created":"2021-05-07","cited_by_count":77,"area":null},{"id":"2203.11819","title":"A Broad Study of Pre-training for Domain Generalization and Adaptation","created":"2022-03-22","cited_by_count":65,"area":null},{"id":"2106.15728","title":"Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles","created":"2021-06-29","cited_by_count":54,"area":null},{"id":"2111.04724","title":"SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning","created":"2021-11-08","cited_by_count":54,"area":null},{"id":"2303.02011","title":"Diagnosing Model Performance Under Distribution Shift","created":"2023-03-03","cited_by_count":39,"area":null},{"id":"2206.07766","title":"Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization","created":"2022-06-15","cited_by_count":38,"area":null},{"id":"2304.11327","title":"Understanding and Improving Feature Learning for Out-of-Distribution Generalization","created":"2023-04-22","cited_by_count":35,"area":null},{"id":"2207.08977","title":"Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift","created":"2022-07-18","cited_by_count":34,"area":null},{"id":"2111.08687","title":"INTERN: A New Learning Paradigm Towards General Vision","created":"2021-11-16","cited_by_count":33,"area":null},{"id":"2204.08491","title":"Active Learning Helps Pretrained Models Learn the Intended Task","created":"2022-04-18","cited_by_count":33,"area":null},{"id":"2307.12532","title":"On the Connection between Pre-training Data Diversity and Fine-tuning Robustness","created":"2023-07-24","cited_by_count":25,"area":null},{"id":"2409.16223","title":"Fine-Tuning is Fine, if Calibrated","created":"2024-09-24","cited_by_count":24,"area":null},{"id":"2107.10400","title":"Species Distribution Modeling for Machine Learning Practitioners: A Review","created":"2021-07-03","cited_by_count":21,"area":null},{"id":"2306.12306","title":"Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift","created":"2023-06-21","cited_by_count":20,"area":null},{"id":"2406.10973","title":"ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts","created":"2024-06-16","cited_by_count":16,"area":null},{"id":"2404.04669","title":"Domain Generalisation via Imprecise Learning","created":"2024-04-06","cited_by_count":14,"area":null},{"id":"2406.07320","title":"A Framework for Efficient Model Evaluation through Stratification, Sampling, and Estimation","created":"2024-06-11","cited_by_count":14,"area":null},{"id":"2405.05160","title":"Selective Classification Under Distribution Shifts","created":"2024-05-08","cited_by_count":12,"area":null},{"id":"2502.14105","title":"Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations","created":"2025-02-19","cited_by_count":12,"area":null},{"id":"2205.12753","title":"An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation","created":"2022-05-25","cited_by_count":11,"area":null},{"id":"2304.12622","title":"Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures","created":"2023-04-25","cited_by_count":11,"area":null},{"id":"2211.09283","title":"Active Learning with Expected Error Reduction","created":"2022-11-17","cited_by_count":9,"area":null},{"id":"2106.15083","title":"ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification","created":"2021-06-29","cited_by_count":8,"area":null},{"id":"2305.05807","title":"Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues","created":"2023-05-09","cited_by_count":8,"area":null},{"id":"2505.14903","title":"When to retrain a machine learning model","created":"2025-05-20","cited_by_count":8,"area":null},{"id":"2212.05149","title":"Stochastic Optimization for Spectral Risk Measures","created":"2022-12-10","cited_by_count":7,"area":null},{"id":"2310.18626","title":"Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness","created":"2023-10-28","cited_by_count":7,"area":null},{"id":"2402.03325","title":"Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations","created":"2024-01-08","cited_by_count":7,"area":null},{"id":"2310.13863","title":"Distributionally Robust Optimization with Bias and Variance Reduction","created":"2023-10-21","cited_by_count":6,"area":null},{"id":"2406.01416","title":"Adapting Prediction Sets to Distribution Shifts Without Labels","created":"2024-06-03","cited_by_count":6,"area":null},{"id":"2506.13831","title":"Quantifying Structure in CLIP Embeddings: A Statistical Framework for Concept Interpretation","created":"2025-06-16","cited_by_count":6,"area":null},{"id":"2403.12236","title":"Improving Generalization via Meta-Learning on Hard Samples","created":"2024-03-18","cited_by_count":5,"area":null},{"id":"2407.01316","title":"Evaluating Model Performance Under Worst-case Subpopulations","created":"2024-07-01","cited_by_count":5,"area":null},{"id":"2503.18987","title":"Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization","created":"2025-03-23","cited_by_count":5,"area":null},{"id":"2507.10425","title":"Non-exchangeable Conformal Prediction with Optimal Transport: Tackling Distribution Shifts with Unlabeled Data","created":"2025-07-14","cited_by_count":5,"area":null},{"id":"2106.03774","title":"Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs","created":"2021-06-07","cited_by_count":4,"area":null},{"id":"2206.02786","title":"(Im)possibility of Collective Intelligence","created":"2022-06-05","cited_by_count":4,"area":null},{"id":"2212.06338","title":"Minimax Optimal Estimation of Stability Under Distribution Shift","created":"2022-12-13","cited_by_count":4,"area":null},{"id":"2401.00608","title":"Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs","created":"2023-12-31","cited_by_count":4,"area":null},{"id":"2402.02851","title":"Enhancing Compositional Generalization via Compositional Feature Alignment","created":"2024-02-05","cited_by_count":4,"area":null},{"id":"2403.16336","title":"Predictive Inference in Multi-environment Scenarios","created":"2024-03-25","cited_by_count":4,"area":null},{"id":"2404.13016","title":"Optimizing Calibration by Gaining Aware of Prediction Correctness","created":"2024-04-19","cited_by_count":4,"area":null},{"id":"2110.12216","title":"Domain Adaptation for Rare Classes Augmented with Synthetic Samples","created":"2021-10-23","cited_by_count":3,"area":null},{"id":"2210.03103","title":"Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!","created":"2022-10-06","cited_by_count":3,"area":null},{"id":"2210.14358","title":"Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations","created":"2022-10-25","cited_by_count":3,"area":null},{"id":"2211.08583","title":"Empirical Study on Optimizer Selection for Out-of-Distribution Generalization","created":"2022-11-15","cited_by_count":3,"area":null},{"id":"2108.12992","title":"SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts","created":"2021-08-30","cited_by_count":2,"area":null},{"id":"2208.02192","title":"RealPatch: A Statistical Matching Framework for Model Patching with Real Samples","created":"2022-08-03","cited_by_count":2,"area":null},{"id":"2211.05236","title":"Okapi: Generalising Better by Making Statistical Matches Match","created":"2022-11-07","cited_by_count":2,"area":null},{"id":"2301.01103","title":"Conservation Tools: The Next Generation of Engineering--Biology Collaborations","created":"2023-01-03","cited_by_count":2,"area":null},{"id":"2308.06686","title":"TorchQL: A Programming Framework for Integrity Constraints in Machine Learning","created":"2023-08-13","cited_by_count":2,"area":null},{"id":"2308.07223","title":"Distance Matters For Improving Performance Estimation Under Covariate Shift","created":"2023-08-14","cited_by_count":2,"area":null},{"id":"2312.02337","title":"Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings","created":"2023-12-04","cited_by_count":2,"area":null},{"id":"2512.15505","title":"The LUMirage: An independent evaluation of zero-shot performance in the LUMIR challenge","created":"2025-12-17","cited_by_count":2,"area":null},{"id":"2106.12212","title":"Image-to-Image Translation of Synthetic Samples for Rare Classes","created":"2021-06-23","cited_by_count":1,"area":null},{"id":"2202.07215","title":"Balancing Domain Experts for Long-Tailed Camera-Trap Recognition","created":"2022-02-15","cited_by_count":1,"area":null},{"id":"2204.03994","title":"LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing","created":"2022-04-08","cited_by_count":1,"area":null},{"id":"2207.07624","title":"Feed-Forward Latent Domain Adaptation","created":"2022-07-15","cited_by_count":1,"area":null},{"id":"2210.12705","title":"Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders","created":"2022-10-23","cited_by_count":1,"area":null},{"id":"2304.11263","title":"Benchmarking Low-Shot Robustness to Natural Distribution Shifts","created":"2023-04-21","cited_by_count":1,"area":null},{"id":"2305.16308","title":"Rectifying Group Irregularities in Explanations for Distribution Shift","created":"2023-05-25","cited_by_count":1,"area":null},{"id":"2507.03146","title":"Set Valued Predictions For Robust Domain Generalization","created":"2025-07-03","cited_by_count":1,"area":null},{"id":"2211.15060","title":"Interactive Visual Feature Search","created":"2022-11-28","cited_by_count":0,"area":null},{"id":"2309.05381","title":"Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations","created":"2023-09-11","cited_by_count":0,"area":null},{"id":"2404.06563","title":"Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows","created":"2024-04-09","cited_by_count":0,"area":null},{"id":"2406.09257","title":"Assessing Model Generalization in Vicinity","created":"2024-06-13","cited_by_count":0,"area":null},{"id":"2509.12265","title":"A Modern Look at Simplicity Bias in Image Classification Tasks","created":"2025-09-13","cited_by_count":0,"area":null},{"id":"2511.16315","title":"BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks","created":"2025-11-20","cited_by_count":0,"area":null},{"id":"2604.01619","title":"Automatic Image-Level Morphological Trait Annotation for Organismal Images","created":"2026-04-02","cited_by_count":0,"area":null},{"id":"2606.05107","title":"Who Needs Labels? Adapting Vision Foundation Models With the Metadata You Already Have","created":"2026-06-03","cited_by_count":0,"area":null}],"next_cursor":null}