Methods › General › Dimensionality Reduction › AE

Autoencoders

AE

292 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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.

Extracted from: Wikipedia

Image source: Wikipedia

Papers archive 2025-07-28

30 shown of 292, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 228 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Anomaly Detection36
Decoder31
Denoising13
Representation Learning13
Dimensionality Reduction12
Clustering10
Deep Learning10
Unsupervised Anomaly Detection9
Adversarial Attack8
Image Generation8
Quantization7
Self-Supervised Learning7
Video Anomaly Detection7
Adversarial Robustness6
Decision Making6
Generative Adversarial Network6
Image Compression6
Transfer Learning6
Autonomous Vehicles5
Classification5

Usage over time archive 2025-07-28

Papers per year tagged with AE: 2013 to 2025, peak 61 61 0 2013: 1 paper 2013 2014: 0 papers 2014 2015: 4 papers 2015 2016: 0 papers 2016 2017: 2 papers 2017 2018: 12 papers 2018 2019: 24 papers 2019 2020: 42 papers 2020 2021: 47 papers 2021 2022: 33 papers 2022 2023: 41 papers 2023 2024: 61 papers 2024 2025: 25 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (292 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Dimensionality Reduction

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