mlsanityData to 7 Oct 2026

A Deeper Look at Dataset Bias

arXiv 1505.01257 ↗First posted 6 May 2015cs.CVcited by 86

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Abstract (arXiv)

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid development of deep learning architectures, the activation values of Convolutional Neural Networks (CNN) are emerging as reliable and robust image descriptors. In this paper we propose to verify the potential of the DeCAF features when facing the dataset bias problem. We conduct a series of analyses looking at how existing datasets differ among each other and verifying the performance of existing debiasing methods under different representations. We learn important lessons on which part of the dataset bias problem can be considered solved and which open questions still need to be tackled.

Key facts. Cited by 86 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 May 2015.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 21; 2022: 22; 2023: 14; 2024: 20; 2025: 6; 2026: 3.

86 papers.

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  1. 2022Kubric: A scalable dataset generatorcited by 373
  2. 2021EnD: Entangling and Disentangling deep representations for bias correctioncited by 105
  3. 2021A Survey on Bias in Visual Datasetscited by 84
  4. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  5. 2021A Prototype-Oriented Framework for Unsupervised Domain Adaptationcited by 62
  6. 2021Understanding and Mitigating Annotation Bias in Facial Expression Recognitioncited by 50
  7. 2024A Decade's Battle on Dataset Bias: Are We There Yet?cited by 46
  8. 2022Unbiased Supervised Contrastive Learningcited by 40
  9. 2021SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Datacited by 31
  10. 2021Unsupervised Monocular Depth Estimation in Highly Complex Environmentscited by 28
  11. 2021Domain-Specific Suppression for Adaptive Object Detectioncited by 26
  12. 2021A Survey on Open Set Recognitioncited by 25
38 more
  1. 2024Training Unbiased Diffusion Models From Biased Datasetcited by 25
  2. 2021Neuro-Symbolic Hierarchical Rule Inductioncited by 23
  3. 2022On the Cross-dataset Generalization in License Plate Recognitioncited by 22
  4. 2022ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognitioncited by 22
  5. 2024Open-World Semantic Segmentation Including Class Similaritycited by 22
  6. 2022A First Look at Dataset Bias in License Plate Recognitioncited by 18
  7. 2024Understanding Bias in Large-Scale Visual Datasetscited by 15
  8. 2023Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognitioncited by 14
  9. 2023CLIP-QDA: An Explainable Concept Bottleneck Modelcited by 13
  10. 2021IFBiD: Inference-Free Bias Detectioncited by 11
  11. 2021An Information Retrieval Approach to Building Datasets for Hate Speech Detectioncited by 9
  12. 2022Analysis of Training Object Detection Models with Synthetic Datacited by 9
  13. 2024Accessing Vision Foundation Models via ImageNet-1Kcited by 9
  14. 2024Unlearnable Examples Detection via Iterative Filteringcited by 9
  15. 2024Efficient Bias Mitigation Without Privileged Informationcited by 8
  16. 2021Detecting discriminatory risk through data annotation based on Bayesian inferencescited by 7
  17. 2024A General Framework for Learning from Weak Supervisioncited by 7
  18. 2021Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptationcited by 6
  19. 2022Through a fair looking-glass: mitigating bias in image datasetscited by 6
  20. 2021Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysiscited by 5
  21. 2021Mitigating Sampling Bias and Improving Robustness in Active Learningcited by 5
  22. 2022A Survey of Dataset Refinement for Problems in Computer Vision Datasetscited by 5
  23. 2024ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfercited by 5
  24. 2024Are Images Indistinguishable to Humans Also Indistinguishable to Classifiers?cited by 5
  25. 2021Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalizationcited by 4
  26. 2022Visualizing the Diversity of Representations Learned by Bayesian Neural Networkscited by 4
  27. 2022Assessing Dataset Bias in Computer Visioncited by 4
  28. 2023Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric researchcited by 4
  29. 2023Mitigating Bias in Visual Transformers via Targeted Alignmentcited by 4
  30. 2024The Visual Experience Dataset: Over 200 Recorded Hours of Integrated Eye Movement, Odometry, and Egocentric Videocited by 4
  31. 2024Mitigating Bias in Dataset Distillationcited by 4
  32. 2022Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Imagescited by 3
  33. 2022Learn to Adapt for Monocular Depth Estimationcited by 3
  34. 2022Can Language Representation Models Think in Bets?cited by 3
  35. 2023Look Beyond Bias with Entropic Adversarial Data Augmentationcited by 3
  36. 2023Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathologycited by 3
  37. 2021Learning Purified Feature Representations from Task-irrelevant Labelscited by 2
  38. 2021Dataset Bias Mitigation Through Analysis of CNN Training Scorescited by 2

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