mlsanityData to 7 Oct 2026

Bias and Generalization in Deep Generative Models: An Empirical Study

arXiv 1811.03259 ↗First posted 8 Nov 2018cs.LG stat.MLcited by 83

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Cited by 26 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Despite recent empirical success, the inductive bias of deep generative models is not well understood. In this paper we propose a framework to systematically investigate bias and generalization in deep generative models of images. Inspired by experimental methods from cognitive psychology, we probe each learning algorithm with carefully designed training datasets to characterize when and how existing models generate novel attributes and their combinations. We identify similarities to human psychology and verify that these patterns are consistent across commonly used models and architectures.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2018.

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Citing papers per year: 2020: 1; 2021: 14; 2022: 14; 2023: 25; 2024: 19; 2025: 7; 2026: 3.

83 papers.

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  1. 2020Denoising Diffusion Implicit Modelscited by 9,655
  2. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
  3. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 121
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  6. 2023TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targetscited by 88
  7. 2023ITI-GEN: Inclusive Text-to-Image Generationcited by 64
  8. 2022Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2cited by 55
  9. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalizationcited by 51
  10. 2021Negative Data Augmentationcited by 46
  11. 2023Supporting Human-AI Collaboration in Auditing LLMs with LLMscited by 46
  12. 2022Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Languagecited by 42
38 more
  1. 2023Spawrious: A Benchmark for Fine Control of Spurious Correlation Biasescited by 41
  2. 2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encodercited by 39
  3. 2023Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformerscited by 33
  4. 2024On the Challenges and Opportunities in Generative AIcited by 31
  5. 2024HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shiftscited by 22
  6. 2022On PAC-Bayesian reconstruction guarantees for VAEscited by 20
  7. 2021Variational Disentanglement for Domain Generalizationcited by 18
  8. 2023Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning modelscited by 18
  9. 2023On the Sample Complexity of Quantum Boltzmann Machine Learningcited by 18
  10. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  11. 2021Local Disentanglement in Variational Auto-Encoders Using Jacobian L1 Regularizationcited by 17
  12. 2021Toward Spatially Unbiased Generative Modelscited by 17
  13. 2023How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Studycited by 16
  14. 2025World Models That Know When They Don't Know - Controllable Video Generation with Calibrated Uncertaintycited by 16
  15. 2023A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Modelscited by 15
  16. 2023Star-Shaped Denoising Diffusion Probabilistic Modelscited by 13
  17. 2024How Diffusion Models Learn to Factorize and Composecited by 13
  18. 2021CoPE: Conditional image generation using Polynomial Expansionscited by 12
  19. 2021Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biasescited by 12
  20. 2023Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-offcited by 12
  21. 2024Fairness and Bias Mitigation in Computer Vision: A Surveycited by 12
  22. 2023Distilling ODE Solvers of Diffusion Models into Smaller Stepscited by 11
  23. 2023Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuningcited by 11
  24. 2024StainDiffuser: MultiTask Dual Diffusion Model for Virtual Stainingcited by 11
  25. 2022Generalization Gap in Amortized Inferencecited by 10
  26. 2022Task Discovery: Finding the Tasks that Neural Networks Generalize oncited by 9
  27. 2021Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifierscited by 8
  28. 2022Generalization Metrics for Practical Quantum Advantage in Generative Modelscited by 8
  29. 2023Conditional Stochastic Interpolation for Generative Learningcited by 8
  30. 2021Pseudo-Spherical Contrastive Divergencecited by 7
  31. 2023Echocardiography video synthesis from end diastolic semantic map via diffusion modelcited by 7
  32. 2024What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Modelscited by 7
  33. 2024On Inductive Biases That Enable Generalization of Diffusion Transformerscited by 7
  34. 2022Self-Conditioned Generative Adversarial Networks for Image Editingcited by 6
  35. 2022Power of Quantum Generative Learningcited by 6
  36. 2022Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approachcited by 6
  37. 2023Vector-based Representation is the Key: A Study on Disentanglement and Compositional Generalizationcited by 6
  38. 2023Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Modelscited by 6

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