mlsanityData to 7 Oct 2026

Tutorial on Variational Autoencoders

arXiv 1606.05908 ↗First posted 19 Jun 2016stat.ML cs.LGcited by 404

Not on the map. Why?

Cited by 46 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits, faces, house numbers, CIFAR images, physical models of scenes, segmentation, and predicting the future from static images. This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior. No prior knowledge of variational Bayesian methods is assumed.

Key facts. Cited by 404 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2016.

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Citing papers per year: 2020: 2; 2021: 79; 2022: 68; 2023: 60; 2024: 94; 2025: 73; 2026: 28.

404 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2024VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Modelscited by 568
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  4. 2021GAN Inversion: A Surveycited by 366
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  6. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
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  8. 2024Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Modelscited by 123
  9. 2021Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoderscited by 109
  10. 2022Tutorial on amortized optimizationcited by 83
  11. 2023Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Modelscited by 82
  12. 2024World Models for Autonomous Driving: An Initial Surveycited by 79
38 more
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  2. 2020Safe Imitation Learning via Fast Bayesian Reward Inference from Preferencescited by 73
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  4. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  5. 2024CV-VAE: A Compatible Video VAE for Latent Generative Video Modelscited by 68
  6. 2021DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Datacited by 62
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  8. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
  9. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  10. 2024Tutorial on Diffusion Models for Imaging and Visioncited by 49
  11. 2023Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEscited by 48
  12. 2021IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantizationcited by 43
  13. 2024HRP: Human Affordances for Robotic Pre-Trainingcited by 40
  14. 2022Reduce Information Loss in Transformers for Pluralistic Image Inpaintingcited by 36
  15. 2024Neural Methods for Amortized Inferencecited by 36
  16. 2021Protein sequence design with deep generative modelscited by 35
  17. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  18. 2023Deep networks for system identification: a Surveycited by 35
  19. 2024Anim-Director: A Large Multimodal Model Powered Agent for Controllable Animation Video Generationcited by 35
  20. 2021Deep reinforcement learning in medical imaging: A literature reviewcited by 34
  21. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  22. 2024SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Datasetcited by 34
  23. 2021Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecastingcited by 32
  24. 2023Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoderscited by 32
  25. 2024WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detectioncited by 31
  26. 2023Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaborationcited by 30
  27. 2022Embrace the Gap: VAEs Perform Independent Mechanism Analysiscited by 28
  28. 2021LILA: Language-Informed Latent Actionscited by 27
  29. 2022Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Modelscited by 27
  30. 2022Hybrid Predictive Coding: Inferring, Fast and Slowcited by 25
  31. 2024TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generationcited by 25
  32. 2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
  33. 2025CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Modelscited by 24
  34. 2021A survey on Variational Autoencoders from a GreenAI perspectivecited by 22
  35. 2022Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Predictioncited by 22
  36. 2022Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognitioncited by 22
  37. 2023Synthetic ECG Signal Generation using Probabilistic Diffusion Modelscited by 22
  38. 2023Invariant Learning via Probability of Sufficient and Necessary Causescited by 22

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