Convolutional Autoencoder

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Convolutional autoencoders study how to compress structured data—such as images, spectra, meshes, and fluid fields—while retaining features useful for reconstruction, denoising, classification, or monitoring. Common approaches train encoder-decoder networks on reconstruction objectives, sometimes with self-supervision, physical constraints, or transfer learning from simulations to experiments. For time-dependent systems, latent codes are paired with predictors such as convolutional LSTMs or neural ODEs, and evaluated on settings including flow forecasting, medical imaging, and spectroscopy. A central tension is that compact codes can reduce computational cost yet lose detail or produce unstable long-horizon forecasts, so bottleneck size and downstream performance must be assessed alongside reconstruction quality.

Latest papers

18 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

Spatially-Aware Self-Organizing Graph Autoencoders via Localized Hebbian Learning
Oct 25, 2026Robin Koç, Hazal Moğultay Özcan, Fatoş T. Yarman VuralVisual Representation Learning Convolutional Autoencoder
Computer Engineering Department, Middle East Technical University, Ankara, Türkiye
Learned Compression of SAR Phase-History Data: A Rate-Honest Feasibility Study on GOTCHA
Sep 25, 2026Alizishaan KhatriSynthetic Aperture Radar Convolutional Autoencoder
Wrynx Inc
Training-Free Bottleneck Width Planning for Convolutional Autoencoders
Sep 25, 2026Guannan GuoConvolutional Autoencoder Dimensionality Reduction
Beihang University
PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning
Sep 23, 2026Kevin Lee, Alison J. MarchInterpretable Anomaly Detection Convolutional Autoencoder
NASA Jet Propulsion Laboratory, La Cañada Flintridge, CA, USA · Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA · College of Engineering & Applied Science, University of Colorado, Boulder, CO, USA
Physics-informed distribution of relaxation times estimation and latent-space condition monitoring of solid oxide fuel and electrolysis cells from electrochemical impedance spectroscopy
Aug 13, 2026Žan Gorenc, Žiga Gradišar, Felix Mütter +2Convolutional Autoencoder Physics-Informed ML
Jožef Stefan International Postgraduate School, Jamova cesta 39, Ljubljana, 1000, Slovenia · Institute of Thermal Engineering, Graz University of Technology, Inffeldgasse 25/B, Graz, 8010, Austria · Jožef Stefan Institute, Jamova cesta 39, Ljubljana, 1000, Slovenia +1
The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows
Jul 27, 2026Alberto Solera-Rico, Patricia García-Caspueñas, Carlos Sanmiguel Vila +1Variational Autoencoders Latent Dynamics Modeling
Sub-directorate general of aeronautical systems, Spanish National Institute for Aerospace Technology (INTA), ctra. M-301, km. 10.5, 28330 San Mart´ın de la Vega, Madrid, Spain. · Departamento de Ingenier´ıa Aeroespacial, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Legan´es, Madrid, Spain. · Department of Mechanical Engineering, University of Washington, 1410 NE Campus Parkway, Seattle, WA 98195, USA.
A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets
Jun 25, 2026Santosh Kapuria, AbhishekConvolutional Autoencoder Transfer Learning
Department of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi, 110016, Delhi, India
A self-supervised learning approach to deep filter banks for texture recognition
May 27, 2026Joao B. Florindo, Lucas O. Lyra, Antonio E. FabrisAutoencoders Convolutional Autoencoder
Institute of Mathematics, Statistics and Scientific Computing of the University of Campinas, Rua Sergio Buarque de Holanda, 651, Campinas, 13083-859, Sao Paulo, Brazil · Institute of Mathematics and Statistics of the University of Sao Paulo, Rua do Matao, 1010, Sao Paulo, 05508-090, Sao Paulo, Brazil
A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy
May 18, 2026David Martin-Calle, Cesar Alvarez Llamas, Vincent Motto- Ros +3Denoising Autoencoders Convolutional Autoencoder
ILM,UCBL,CNRS · Universit´e Claude Bernard Lyon 1, CNRS, Institut Lumi`ere Mati`ere, F-69622 Villeurbanne, France · ILM,UCBL,CNRS,IUF +1
Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields
May 11, 2026Haechan Mark Bong, Giovanni BeltrameUAV Navigation Convolutional Autoencoder
Department of Computer Engineering and Software Engineering, Polytechnique Montréal, QC., Canada. · MILA, QC., Canada.
Focus on What Matters: Two-Stage ROI-Aware Refinement for Anatomy-Preserving Fetal Ultrasound Reconstruction
Apr 26, 2026Ines Abbes, Mahmood Alzubaidi, Mowafa Househ +3Autoencoders Image Reconstruction
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar · Sidra Medicine, Doha, Qatar
PAWN: Piece Value Analysis with Neural Networks
Apr 16, 2026Ethan Tang, Hasan Davulcu, Jia Zou +1Convolutional Autoencoder Chess
Arizona State University, Tempe AZ 85281, USA
A convolutional autoencoder and neural ODE surrogate modeling framework applied to transient counterflow flames
Mar 16, 2026Mert Yakup Baykan, Weitao Liu, Mohammad Rafi Malik +4Autoencoders PDE 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
Embedding interpretable $\ell_1$ -regression into neural networks for uncovering temporal structure in cell imaging
Mar 3, 2026Fabian Kabus, Maren Hackenberg, Julia Hindel +6Autoregressive Modeling Neural Network Interpretability
Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Germany · Department of Computer Science, University of Freiburg, Germany · Institute of Physiology I, Faculty of Medicine, University of Freiburg, Germany +2
A 3D mesh convolution-based autoencoder for geometry compression
Mar 2, 2026Germain Bregeon, Marius Preda, Radu Ispas +1Convolutional Autoencoder 3D Mesh Reconstruction
Telecom SudParis, Institut Polytechnique de Paris, France · Keyrus, Paris, France
CLAP Convolutional Lightweight Autoencoder for Plant Disease Classification
Feb 21, 2026Asish Bera, Subhajit Roy, Sudiptendu BanerjeeDepthwise Separable Convolution Plant Disease Classification
Department of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, Rajasthan, India
Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks
Feb 20, 2026Kameswara Bharadwaj Mantha, Lucy Fortson, Ramanakumar Sankar +8Unsupervised Representation Learning Convolutional Autoencoder
Department of Physics and Astronomy, University of Minnesota Twin Cities, Minneapolis, USA · Company Name, Location, Country · European Space Agency (ESA), European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n 28692 Villanueva de la Cañada, Madrid, Spain +4
Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders
May 19, 2025Fynn Fromme, Hans Harder, Christine Allen-Blanchette +1Autoencoders PDE Surrogate Modeling
Lamarr Institute for Machine Learning and Artificial Intelligence and Karlsruhe Institute of Technology · Lamarr Institute for Machine Learning and Artificial Intelligence and Department of Computer Science at Paderborn University · Department of Mechanical and Aerospace Engineering Center for Statistics and Machine Learning Princeton University +1

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Spatially-Aware Self-Organizing Graph Autoencoders via Localized Hebbian Learning

Robin Koç, Hazal Moğultay Özcan, Fatoş T. Yarman Vural

Computer Engineering Department, Middle East Technical University, Ankara, Türkiye

Visual Representation Learning Convolutional Autoencoder Graph Structure Learning Graph Neural Networks Hebbian Learning

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Traditional autoencoders rely on static network architectures to learn latent representations. Recently we suggested a dynamic architecture inspired by Hebbian plasticity, enabling topological self-organization among convolutional feature maps (R. Koç et al., Proceedings of the SIU, 2026). Early implementations of this paradigm infer relationships among feature maps using global average pooling and model their interactions through scalar-valued connections. This approach falls short to fully exploit the underlying spatial information, embedded in input images. Furthermore, abrupt transitions between different loss functions and training dynamics can introduce representation shocks, leading to unstable feature learning. In this paper, we introduce a Spatially-Aware Self-Organizing Graph Autoencoder (S-LooM) to address these structural bottlenecks. By using spatial cross-correlation, and spatial kernels, S-LooM establishes dynamic connections among feature maps. We replace static scalar message passing of autoencoders with learnable spatial kernels and eliminate high-frequency deconvolutional artifacts observed in decoders using nearest-neighbor upsampling. Via a soft-blending training curriculum, the latent topology aligns smoothly during training. Our experiments show that S-LooM yields a more stable topology, compared to the state of the art dynamic architectures. Meaning, the graph structure that emerge has more resistance to feature entanglement and noise. It forms functional communities responsible for visual attributes, such as illumination, shape and texture even under high connection densities.