mlsanityData to 14 Oct 2026

Adversarial Autoencoders

arXiv 1511.05644 ↗First posted 18 Nov 2015cs.LGcited by 364

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Abstract (arXiv)

In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution. Matching the aggregated posterior to the prior ensures that generating from any part of prior space results in meaningful samples. As a result, the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution. We show how the adversarial autoencoder can be used in applications such as semi-supervised classification, disentangling style and content of images, unsupervised clustering, dimensionality reduction and data visualization. We performed experiments on MNIST, Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi-supervised classification tasks.

Key facts. Cited by 364 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2015.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 2; 2021: 109; 2022: 84; 2023: 70; 2024: 42; 2025: 38; 2026: 18.

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  1. 2021GAN Inversion: A Surveycited by 367
  2. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 252
  3. 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
  4. 2018Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flowcited by 105
  5. 2021Deep Generative Learning via Schr"odinger Bridgecited by 104
  6. 2021SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learningcited by 104
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  8. 2025EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modelingcited by 89
  9. 2022Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fieldscited by 85
  10. 2021SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarioscited by 84
  11. 2022Deep Clustering: A Comprehensive Surveycited by 78
  12. 2023To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Reviewcited by 72
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  2. 2023Recent Advances in Optimal Transport for Machine Learningcited by 66
  3. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  4. 2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
  5. 2023A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyondcited by 55
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  7. 2021Style Normalization and Restitution for Domain Generalization and Adaptationcited by 46
  8. 2022Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoderscited by 42
  9. 2021Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learningcited by 39
  10. 2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
  11. 2022GATSBI: Generative Adversarial Training for Simulation-Based Inferencecited by 36
  12. 2022Neural Optimal Transport with General Cost Functionalscited by 36
  13. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  14. 2021EigenGAN: Layer-Wise Eigen-Learning for GANscited by 34
  15. 2021High-order Differentiable Autoencoder for Nonlinear Model Reductioncited by 32
  16. 2022Computer-Aided Multi-Objective Optimization in Small Molecule Discoverycited by 31
  17. 2021Unsupervised Skill Discovery with Bottleneck Option Learningcited by 28
  18. 2022Synthetic Data in Human Analysis: A Surveycited by 28
  19. 2022Efficient Self-supervised Vision Pretraining with Local Masked Reconstructioncited by 27
  20. 2021Towards Generating Real-World Time Series Datacited by 26
  21. 2022SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminatorscited by 26
  22. 2022Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Casecited by 26
  23. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 26
  24. 2021HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximizationcited by 25
  25. 2021Towards Defending against Adversarial Examples via Attack-Invariant Featurescited by 25
  26. 2021Diffusion Priors In Variational Autoencoderscited by 25
  27. 2021Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathologycited by 25
  28. 2023A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRUcited by 25
  29. 2021Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learningcited by 24
  30. 2021Adversarially Robust One-class Novelty Detectioncited by 24
  31. 2022A Geometric Perspective on Variational Autoencoderscited by 24
  32. 2022On the Domain Adaptation and Generalization of Pretrained Language Models: A Surveycited by 23
  33. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  34. 2021Creativity and Machine Learning: A Surveycited by 21
  35. 2022A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Imagescited by 21
  36. 2023Identifying Representations for Intervention Extrapolationcited by 21
  37. 2020Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitationscited by 20
  38. 2021Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Predictioncited by 20

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