mlsanityData to 7 Oct 2026

Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational Autoencoder

arXiv 2309.02084 ↗First posted 5 Sep 2023cs.LGcited by 0

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Abstract (arXiv)

Deep generative models have been demonstrated as problematic in the unsupervised out-of-distribution (OOD) detection task, where they tend to assign higher likelihoods to OOD samples. Previous studies on this issue are usually not applicable to the Variational Autoencoder (VAE). As a popular subclass of generative models, the VAE can be effective with a relatively smaller model size and be more stable and faster in training and inference, which can be more advantageous in real-world applications. In this paper, We propose a novel VAE-based score called Error Reduction (ER) for OOD detection, which is based on a VAE that takes a lossy version of the training set as inputs and the original set as targets. Experiments are carried out on various datasets to show the effectiveness of our method, we also present the effect of design choices with ablation experiments. Our code is available at: https://github.com/ZJLAB-AMMI/VAE4OOD.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 5 Sep 2023.

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References · 28

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  6. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  7. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  8. 2013Describing Textures in the Wildcited by 2,110
  9. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  10. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  11. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  12. 2016Pixel Recurrent Neural Networkscited by 814
16 more
  1. 2015Generating Sentences from a Continuous Spacecited by 607
  2. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  3. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  4. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  5. 2018Deep Learning for Classical Japanese Literaturecited by 415
  6. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  7. 2019Your Classifier is Secretly an Energy Based Model and You Should Treat it Like Onecited by 402
  8. 2019Input complexity and out-of-distribution detection with likelihood-based generative modelscited by 186
  9. 2016Variational Lossy Autoencodercited by 175
  10. 2020Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encodercited by 145
  11. 2019BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modelingcited by 109
  12. 2021Understanding Failures in Out-of-Distribution Detection with Deep Generative Modelscited by 89
  13. 2018Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distancecited by 74
  14. 2020Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Featurescited by 64
  15. 2021Hierarchical VAEs Know What They Don't Knowcited by 58
  16. 2022Out-of-distribution Detection via Frequency-regularized Generative Modelscited by 23

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