mlsanityData to 7 Oct 2026

A Note on the Inception Score

arXiv 1801.01973 ↗First posted 6 Jan 2018stat.ML cs.LGcited by 225

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Cited by 67 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep generative models are powerful tools that have produced impressive results in recent years. These advances have been for the most part empirically driven, making it essential that we use high quality evaluation metrics. In this paper, we provide new insights into the Inception Score, a recently proposed and widely used evaluation metric for generative models, and demonstrate that it fails to provide useful guidance when comparing models. We discuss both suboptimalities of the metric itself and issues with its application. Finally, we call for researchers to be more systematic and careful when evaluating and comparing generative models, as the advancement of the field depends upon it.

Key facts. Cited by 225 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2018.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 1; 2021: 31; 2022: 32; 2023: 43; 2024: 53; 2025: 47; 2026: 17.

225 papers.

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  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  2. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  3. 2023ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generationcited by 1,010
  4. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  5. 2018Scalable agent alignment via reward modeling: a research directioncited by 438
  6. 2023Rethinking FID: Towards a Better Evaluation Metric for Image Generationcited by 303
  7. 2023Human Preference Score: Better Aligning Text-to-Image Models with Human Preferencecited by 284
  8. 2022Cold Diffusion: Inverting Arbitrary Image Transforms Without Noisecited by 275
  9. 2021Cross-Modal Contrastive Learning for Text-to-Image Generationcited by 259
  10. 2023Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioningcited by 254
  11. 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 193
  12. 2023Denoising Diffusion Bridge Modelscited by 171
38 more
  1. 2022KNN-Diffusion: Image Generation via Large-Scale Retrievalcited by 135
  2. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 121
  3. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
  4. 2023HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Modelscited by 94
  5. 2024Golden Noise for Diffusion Models: A Learning Frameworkcited by 91
  6. 2022PoseGPT: Quantization-based 3D Human Motion Generation and Forecastingcited by 78
  7. 2024PTQ4DiT: Post-training Quantization for Diffusion Transformerscited by 77
  8. 2022A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Imagescited by 74
  9. 2023LDM3D: Latent Diffusion Model for 3Dcited by 53
  10. 2021Adversarial Text-to-Image Synthesis: A Reviewcited by 52
  11. 2022Efficient Diffusion Models for Vision: A Surveycited by 50
  12. 2024QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuningcited by 48
  13. 2024Diffusion Bridge Implicit Modelscited by 47
  14. 2022Denoising Likelihood Score Matching for Conditional Score-based Data Generationcited by 45
  15. 2023Designing a Better Asymmetric VQGAN for StableDiffusioncited by 45
  16. 2021Unifying Multimodal Transformer for Bi-directional Image and Text Generationcited by 41
  17. 2022Self-Guided Diffusion Modelscited by 36
  18. 2022A Study on the Evaluation of Generative Modelscited by 35
  19. 2021ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priorscited by 34
  20. 2022PyTorch Image Quality: Metrics for Image Quality Assessmentcited by 34
  21. 2025VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual interventioncited by 33
  22. 2022Full-band General Audio Synthesis with Score-based Diffusioncited by 32
  23. 2023BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inferencecited by 30
  24. 2025Halton Scheduler For Masked Generative Image Transformercited by 30
  25. 2025Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewardscited by 29
  26. 2022Diagnosing and Fixing Manifold Overfitting in Deep Generative Modelscited by 28
  27. 2023Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generationcited by 28
  28. 2023DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathologycited by 28
  29. 2023RSDiff: Remote Sensing Image Generation from Text Using Diffusion Modelcited by 28
  30. 2024Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Modelcited by 27
  31. 2022Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Casecited by 26
  32. 2024SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuningcited by 26
  33. 2023Measuring the Quality of Text-to-Video Model Outputs: Metrics and Datasetcited by 23
  34. 2023Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Designcited by 22
  35. 2024Consistency Diffusion Bridge Modelscited by 22
  36. 2025Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminatorcited by 22
  37. 2025AR-GRPO: Training Autoregressive Image Generation Models via Reinforcement Learningcited by 22
  38. 2021TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimationcited by 20

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