Autoencoders

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Overview

Autoencoders learn a compact or structured latent representation by encoding data and training a decoder to reconstruct it, supporting dimensionality reduction, compression, and feature learning without labels. Common approaches include convolutional, recurrent, and transformer autoencoders; variational and sparse variants; and models with geometric, physical, or domain-specific constraints. They are used on images, audio and video, scientific simulations, medical data, and industrial time series, with evaluation ranging from reconstruction and compression to anomaly detection and downstream prediction. A central tension is that latents optimized for faithful reconstruction are not always useful for generation, interpretation, or stable long-horizon prediction, so current work increasingly evaluates and shapes representations for their intended downstream use.

Latest papers

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155 papers23 papers in the last four weeks, up 64% on the four weeks before. 0.1% of all new papers.

  1. Manifold-Matching Autoencoders

    Mar 17, 2026Laurent Cheret, Vincent Létourneau, Isar Nejadgholi +3Multidimensional ScalingAutoencoders

    Department of Computer Science, University of Ottawa, Ottawa, Canada · MILA, Montréal, Canada · National Research Council of Canada, Ottawa, Canada

  2. A convolutional autoencoder and neural ODE surrogate modeling framework applied to transient counterflow flames

    Mar 16, 2026Mert Yakup Baykan, Weitao Liu, Mohammad Rafi Malik +4AutoencodersPDE Surrogate Modeling

    Department of Aerospace Engineering, KAIST, Daejeon, Republic of Korea · Clean Energy Research Platform (CERP), KAUST, Thuwal, Saudi Arabia · Institute for Reactive Flows (IRST), University of Stuttgart, Pfaffenwaldring 31, 70569 Stuttgart, Germany

  3. Is the reconstruction loss culprit? An attempt to outperform JEPA

    Mar 14, 2026Alexey Potapov, Oleg Shcherbakov, Ivan KravchenkoPredictive Representation LearningAutoencoders

    SingularityNET

  4. Exploiting temporal parallelism for LSTM Autoencoder acceleration on FPGA

    Mar 14, 2026Aimilios Leftheriotis, Dimosthenis Masouros, Dimitrios Soudris +1FPGA-Based Neural Network AccelerationEfficient Neural Network Inference

    University of Patras, Patras, Greece · National Technical University of Athens, Athens, Greece

  5. MO-SAE:Multi-Objective Stacked Autoencoders Optimization for Edge Anomaly Detection

    Mar 14, 2026Lizhao Zhang, Shengsong Kong, Tao Guo +2AutoencodersEfficient Neural Network Inference

    Faculty of computing, Harbin Institute of Technology, Harbin 150000, China.

  6. Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems

    Mar 13, 2026Nicolò Botteghi, Silke Glas, Christoph BruneAutoencodersPDE Surrogate Modeling

    1MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milano, Italy · Department of Applied Mathematics, University of Twente, Enschede, Netherlands

  7. Context-dependent manifold learning: A neuromodulated constrained autoencoder approach

    Mar 12, 2026Jérôme Adriaens, Gustave Bainier, Guillaume Drion +1AutoencodersManifold Learning

    Neuroengineering Lab, Department of Electrical Engineering and Computer Science, University of Liège · University of Liège, Liège, Belgium

  8. A Stable Neural Statistical Dependence Estimator for Autoencoder Feature Analysis

    Mar 12, 2026Bo Hu, Jose C PrincipeMutual Information EstimationAutoencoders

    Department of Electrical and Computer Engineering, University of Florida

  9. Domain-Adaptive Health Indicator Learning with Degradation-Stage Synchronized Sampling and Cross-Domain Autoencoder

    Mar 11, 2026Jungho Choo, Hanbyeol Park, Gawon Lee +2Predictive MaintenanceAutoencoders

    Major in Industrial Data Science Engineering, Department of Industrial Engineering, Pusan National University, Busan, 46241, Republic of Korea · C4ISTAR IPS R&D LIG Nex1, Pangyo, 13488, Republic of Korea · Department of Data Science, Graduate School of Data Science, Pusan National University, Busan, 46241, Republic of Korea

  10. IAML: Illumination-Aware Mirror Loss for Progressive Learning in Low-Light Image Enhancement Auto-encoders

    Mar 9, 2026Farida Mohsen, Tala Zaim, Ali Al-Zawqari +2Low-Light Image EnhancementAutoencoders

    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar. · ELEC department, Vrije Universiteit Brussel, Brussels, Belgium.

  11. Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss

    Mar 8, 2026Ruixin Guo, Xinyu Li, Hao Zhou +2Recommender SystemsAutoencoders

    Kent State University · Auburn University

  12. CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing

    Mar 6, 2026Mohammadhossein Ghahramani, Mengchu ZhouSmart ManufacturingIndustrial Fault Diagnosis

    Birmingham City University, UK · the Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA

  13. Implementation of Quantum Implicit Neural Representation in Deterministic and Probabilistic Autoencoders for Image Reconstruction/Generation Tasks

    Mar 6, 2026Saadet Müzehher ErenVariational AutoencodersQuantum Generative Modeling

    Department of Physics, Izmir Institute of Technology, Gülbahçe, Urla, 35430, Izmir, Turkey.

  14. RAC: Rectified Flow Auto Coder

    Mar 6, 2026Sen Fang, Yalin Feng, Yanxin Zhang +6AutoencodersVariational Autoencoders

  15. On Geometry Regularization in Autoencoder Reduced-Order Models with Latent Neural ODE Dynamics

    Mar 3, 2026Mikhail OsipovReduced-Order ModelingAutoencoders

    Independent Researcher, Italy

  16. Unmixing ATR-μFTIR spectroscopic images of cross-sections of historical oil paintings

    Mar 3, 2026Shivam Pande, Nicolas Nadisic, Francisco Mederos-Henry +1Hyperspectral UnmixingHyperspectral Imaging

    Department of Telecommunications and Information Processing, Ghent University, Belgium. · Royal Institute for Cultural Heritage (KIK-IRPA), Belgium. · Engineering of Molecular Nanosystems (EMNS) Laboratory, Université Libre de Bruxelles (ULB), Belgium.

  17. FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation

    Mar 2, 2026Hanxiao Wang, Yuan-Chen Guo, Ying-Tian Liu +63D Mesh GenerationAutoencoders

    CASIA · UCAS · VAST +1

  18. Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlases

    Feb 26, 2026Eduardo Paluzo-Hidalgo, Yuichi IkeManifold LearningAutoencoders

    Department of Applied Mathematics I, University of Sevilla, Sevilla, Spain · Graduate School of Mathematical Sciences, The University of Tokyo, Tokyo, Japan

  19. The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA

    Feb 23, 2026Zien Ma, S. M. Shermer, Oktay Karakuş +1Magnetic Resonance SpectroscopySim-to-Real Transfer

    School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 4AG, United Kingdom · Faculty of Science and Engineering, Swansea University, Swansea, SA2 8PP, United Kingdom

  20. Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks

    Feb 18, 2026Sarkis Ter Martirosyan, Xinyue Huang, David Qin +2Spectral UnmixingPhotoacoustic Tomography

    School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States · Institute of Biological and Medical Imaging, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany · Chair of Biological Imaging, Technische Universität München, 80333 München, Germany +1

  21. A Generative-First Neural Audio Autoencoder

    Feb 17, 2026Jonah Casebeer, Ge Zhu, Zhepei Wang +1AutoencodersAudio Representation Learning

    Adobe Research

  22. LEADER: Lightweight End-to-End Attention-Gated Dual Encoder-Decoder Network for Robust Minutiae Extraction

    Feb 17, 2026Raffaele Cappelli, Matteo FerraraFingerprint IdentificationBiometric Identification

    Department of Computer Science and Engineering of the University of Bologna, Italy

  23. PolyNODE: Variable-dimension Neural ODEs on M-polyfolds

    Feb 16, 2026Per Åhag, Alexander Friedrich, Fredrik Ohlsson +1Neural ODEsAutoencoders

    Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden

  24. Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

    Feb 10, 2026David S. Duque-Castaño, Lauren Flor-Torres, Jorge I. ZuluagaDenoising AutoencodersAutoencoders

    SEAP/FACom, Instituto de Física - FCEN, Universidad de Antioquia, Calle 70 No. 52-21, Medellín, Colombia

  25. Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification

    Feb 3, 2026Jack Michael Solomon, Rishi Leburu, Matthias ChungVariational AutoencodersUncertainty Quantification

    Emory University Department of Mathematics

  26. Verification of Unknown Dynamical Systems via Autoencoder Latent Space

    Dec 15, 2025Robert Reed, Luca Laurenti, Morteza LahijanianFormal VerificationState Abstraction

    Dept. of Aerospace Engineering University of Colorado Boulder Boulder, Colorado, USA · Delft University of Technology Delft, Netherlands · AI4I, Turin, Italy

  27. FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds

    Dec 10, 2025Marco Pegoraro, Evan Atherton, Bruno Roy +2Representation LearningAutoencoders

    Institute of Science and Technology Austria · Autodesk Research

  28. A Machine Learning-Driven Solution for Denoising Inertial Confinement Fusion Images

    Nov 20, 2025Asya Y. Akkus, Bradley T. Wolfe, Pinghan Chu +7AutoencodersImage Denoising

    This work was conducted under the auspices of the DOE SULI and Los Alamos National Laboratory LDRD programs and by LLNL under Contract DE-AC52-07NA27344.

  29. Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders

    Nov 7, 2025Mathias Rose Bjare, Giorgia Cantisani, Marco Pasini +2Audio Representation LearningDenoising Autoencoders

    Johannes Kepler University, Linz, AT · STMS, CNRS, IRCAM, Sorbonne Universit´e, Paris, FR · Queen Mary University, London, UK +1

  30. Sculpting the Latent Space: Disentanglement with WAE-MMD and Programmable Priors

    Oct 13, 2025Quentin Fruytier, Akshay Malhotra, Shahab Hamidi-Rad +3Disentangled Representation LearningSemantic Representation Learning

    The University of Texas at Austin · InterDigital Communications, AI Lab