{"total":21,"rows":[{"id":"2203.01517","title":"Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations","created":"2022-03-03","cited_by_count":172,"area":null},{"id":"2306.11074","title":"Simple and Fast Group Robustness by Automatic Feature Reweighting","created":"2023-06-19","cited_by_count":68,"area":null},{"id":"2210.00055","title":"MaskTune: Mitigating Spurious Correlations by Forcing to Explore","created":"2022-09-30","cited_by_count":66,"area":null},{"id":"2207.07180","title":"Contrastive Adapters for Foundation Model Group Robustness","created":"2022-07-14","cited_by_count":62,"area":null},{"id":"2305.18761","title":"Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias","created":"2023-05-30","cited_by_count":44,"area":null},{"id":"2306.04949","title":"Robust Learning with Progressive Data Expansion Against Spurious Correlation","created":"2023-06-08","cited_by_count":39,"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":"2402.13368","title":"Unsupervised Concept Discovery Mitigates Spurious Correlations","created":"2024-02-20","cited_by_count":11,"area":null},{"id":"2504.07092","title":"Are We Done with Object-Centric Learning?","created":"2025-04-09","cited_by_count":11,"area":null},{"id":"2403.02695","title":"Controllable Prompt Tuning For Balancing Group Distributional Robustness","created":"2024-03-05","cited_by_count":10,"area":null},{"id":"2503.09487","title":"Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness","created":"2025-03-12","cited_by_count":9,"area":null},{"id":"2409.17691","title":"Efficient Bias Mitigation Without Privileged Information","created":"2024-09-26","cited_by_count":8,"area":null},{"id":"2210.02748","title":"CLAD: A Contrastive Learning based Approach for Background Debiasing","created":"2022-10-06","cited_by_count":6,"area":null},{"id":"2309.06717","title":"Bias Amplification Enhances Minority Group Performance","created":"2023-09-13","cited_by_count":6,"area":null},{"id":"2311.17327","title":"Improving Self-supervised Molecular Representation Learning using Persistent Homology","created":"2023-11-29","cited_by_count":6,"area":null},{"id":"2407.05594","title":"SLIM: Spuriousness Mitigation with Minimal Human Annotations","created":"2024-07-08","cited_by_count":5,"area":null},{"id":"2111.14271","title":"ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification","created":"2021-11-28","cited_by_count":2,"area":null},{"id":"2602.11785","title":"Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2405.14030","title":"Refining Skewed Perceptions in Vision-Language Contrastive Models through Visual Representations","created":"2024-05-22","cited_by_count":0,"area":null},{"id":"2502.11031","title":"A Critical Review of Predominant Bias in Neural Networks","created":"2025-02-16","cited_by_count":0,"area":null},{"id":"2606.16210","title":"Sensor-Conditioned Representation Learning via Scene-Relevant Observation Quotients","created":"2026-06-15","cited_by_count":0,"area":null}],"next_cursor":null}