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AE

Autoencoders

GeneralIntroduced 2000293 papers

Description

An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name.

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Papers Using This Method

Meta-autoencoders: An approach to discovery and representation of relationships between dynamically evolving classes2025-07-12 Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark and Lessons Learned2025-06-19 Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders2025-06-17 Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network2025-05-26 Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration2025-05-14 DHECA-SuperGaze: Dual Head-Eye Cross-Attention and Super-Resolution for Unconstrained Gaze Estimation2025-05-13 ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders2025-05-06 Seeking Flat Minima over Diverse Surrogates for Improved Adversarial Transferability: A Theoretical Framework and Algorithmic Instantiation2025-04-23 TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histology2025-04-17 H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models2025-04-14 Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images2025-04-04 Nonlinear Multiple Response Regression and Learning of Latent Spaces2025-03-27 Unsupervised Joint Learning of Optical Flow and Intensity with Event Cameras2025-03-21 EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data2025-03-18 Pathology Image Compression with Pre-trained Autoencoders2025-03-14 Weakly Supervised Contrastive Adversarial Training for Learning Robust Features from Semi-supervised Data2025-03-14 Understanding the role of autoencoders for stiff dynamical systems using information theory2025-03-08 From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints2025-03-06 Joint Beamforming Design for Integrated Sensing and Communication Systems with Hybrid-Colluding Eavesdroppers2025-02-07 CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition2025-02-06