mlsanityData to 14 Oct 2026

PIE: Pseudo-Invertible Encoder

arXiv 2111.00619 ↗First posted 31 Oct 2021cs.LG cs.CVcited by 5

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Abstract (arXiv)

We consider the problem of information compression from high dimensional data. Where many studies consider the problem of compression by non-invertible transformations, we emphasize the importance of invertible compression. We introduce new class of likelihood-based autoencoders with pseudo bijective architecture, which we call Pseudo Invertible Encoders. We provide the theoretical explanation of their principles. We evaluate Gaussian Pseudo Invertible Encoder on MNIST, where our model outperforms WAE and VAE in sharpness of the generated images.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 12 of its 16 references that are in the map. First posted on arXiv 31 Oct 2021.

Builds on

12 of 16 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2013Auto-Encoding Variational Bayescited by 4,053
  4. 2015Variational Inference with Normalizing Flowscited by 2,170
  5. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  6. 2016Density estimation using Real NVPcited by 2,042
  7. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
  8. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  9. 2015Deep Learning and the Information Bottleneck Principlecited by 802
  10. 2017Wasserstein Auto-Encoderscited by 373
  11. 2017The Reversible Residual Network: Backpropagation Without Storing Activationscited by 217
  12. 2018i-RevNet: Deep Invertible Networkscited by 137

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 0; 2025: 0; 2026: 2.

5 papers.

  1. 2023Lifting Architectural Constraints of Injective Flowscited by 18
  2. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  3. 2021Manifold embedding data-driven mechanicscited by 2
  4. 2026Pseudo-Invertible Neural Networkscited by 2
  5. 2026NAE: Normalizing AutoEncodercited by 0