mlsanityData to 7 Oct 2026

Generative Interventions for Causal Learning

arXiv 2012.12265 ↗First posted 22 Dec 2020cs.CV cs.LGcited by 45

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Abstract (arXiv)

We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally occurring spurious correlations, which cause them to fail on images outside of the training distribution. In this paper, we show that we can steer generative models to manufacture interventions on features caused by confounding factors. Experiments, visualizations, and theoretical results show this method learns robust representations more consistent with the underlying causal relationships. Our approach improves performance on multiple datasets demanding out-of-distribution generalization, and we demonstrate state-of-the-art performance generalizing from ImageNet to ObjectNet dataset.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2020.

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Citing papers per year: 2021: 6; 2022: 8; 2023: 17; 2024: 6; 2025: 2; 2026: 6.

45 papers.

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  1. 2022Fake it till you make it: Learning transferable representations from synthetic ImageNet clonescited by 171
  2. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
  3. 2021GAN-Supervised Dense Visual Alignmentcited by 61
  4. 2021Ensembling with Deep Generative Viewscited by 53
  5. 2023Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactualscited by 44
  6. 2021Discrete Representations Strengthen Vision Transformer Robustnesscited by 34
  7. 2022OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networkscited by 33
  8. 2024Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Modelscited by 32
  9. 2022Causal Reasoning Meets Visual Representation Learning: A Prospective Studycited by 24
  10. 2023Self-Interpretable Time Series Prediction with Counterfactual Explanationscited by 24
  11. 2022Causal Transportability for Visual Recognitioncited by 23
  12. 2022General Intelligence Requires Rethinking Explorationcited by 20
33 more
  1. 2023Causal Deep Learningcited by 20
  2. 2023CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Imagescited by 19
  3. 2021LARGE: Latent-Based Regression through GAN Semanticscited by 17
  4. 2023Convolutional Visual Prompt for Robust Visual Perceptioncited by 17
  5. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  6. 2023Data Augmentations for Improved (Large) Language Model Generalizationcited by 14
  7. 2024GDA: Generalized Diffusion for Robust Test-time Adaptationcited by 14
  8. 2023Causal reasoning in typical computer vision taskscited by 12
  9. 2023Interpreting and Controlling Vision Foundation Models via Text Explanationscited by 11
  10. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  11. 2021Instrumental Variable-Driven Domain Generalization with Unobserved Confounderscited by 7
  12. 2022Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classificationcited by 7
  13. 2023Instance-Conditioned GAN Data Augmentation for Representation Learningcited by 7
  14. 2023Rating Sentiment Analysis Systems for Bias through a Causal Lenscited by 6
  15. 2023Advances in Automatically Rating the Trustworthiness of Text Processing Servicescited by 6
  16. 2023RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learningcited by 6
  17. 20223D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspectivecited by 5
  18. 2023Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopycited by 5
  19. 2024The Effect of Human v/s Synthetic Test Data and Round-tripping on Assessment of Sentiment Analysis Systems for Biascited by 4
  20. 2025DAViD: Modeling Dynamic Affordance of 3D Objects Using Pre-trained Video Diffusion Modelscited by 4
  21. 2024Consistency of Neural Causal Partial Identificationcited by 3
  22. 2022A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signalscited by 2
  23. 2023Implicit Counterfactual Data Augmentation for Robust Learningcited by 2
  24. 2023TC-VAE: Uncovering Out-of-Distribution Data Generative Factorscited by 1
  25. 2024Revisiting Spurious Correlation in Domain Generalizationcited by 1
  26. 2026Causal Neighbourhood Learning for Invariant Graph Representationscited by 1
  27. 2024Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causalitycited by 0
  28. 2025Mitigating Spurious Correlations with Causal Logit Perturbationcited by 0
  29. 2026Mitigating Shortcut Learning via Feature Disentanglement in Medical Imaging: A Benchmark Studycited by 0
  30. 2026Causal Decoding for Hallucination-Resistant Multimodal Large Language Modelscited by 0
  31. 2026Learning domain-invariant features through channel-level sparsification for Out-Of Distribution Generalizationcited by 0
  32. 2026Synthetic Designed Experiments for Diagnosing Vision Model Failurecited by 0
  33. 2026A Data-Interventional Framework for Auditing Privacy and Fairness in Generative Medical Imagingcited by 0