Methods › Computer Vision › Generative Models › Contractive Autoencoder

Contractive Autoencoder

introduced 2011 5 papers tagged archive 2025-07-28

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

A Contractive Autoencoder is an autoencoder that adds a penalty term to the classical reconstruction cost function. This penalty term corresponds to the Frobenius norm of the Jacobian matrix of the encoder activations with respect to the input. This penalty term results in a localized space contraction which in turn yields robust features on the activation layer. The penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold.

Papers archive 2025-07-28

5 shown of 5, 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

5 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
Denoising2
Representation Learning2
Semantic Segmentation2
Dictionary Learning1
Motion Planning1

Usage over time archive 2025-07-28

Papers per year tagged with Contractive Autoencoder: 2014 to 2022, peak 1 1 0 2014: 1 paper 2014 2015: 0 papers 2015 2016: 0 papers 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 1 paper 2019 2020: 1 paper 2020 2021: 1 paper 2021 2022: 1 paper 2022
Papers per year the archive tags with this method, by the paper's archive date (5 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

Generative Models

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