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ControlVAE

3 papers tagged archive 2025-07-28

Introduced by Huajie Shao et al. in ControlVAE: Tuning, Analytical Properties, and Performance Analysis

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

ControlVAE is a variational autoencoder (VAE) framework that combines the automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value. It leverages a non-linear PI controller, a variant of the proportional-integral-derivative (PID) control, to dynamically tune the weight of the KL-divergence term in the evidence lower bound (ELBO) using the output KL-divergence as feedback. This allows for control of the KL-divergence to a desired value (set point), which is effective in avoiding posterior collapse and learning disentangled representations.

Paper Source

Papers archive 2025-07-28

3 shown of 3, 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.

ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters
12 Oct 2022 · 0 repositories · arXiv:2210.06063
Rethinking Controllable Variational Autoencoders
1 Jan 2022 · 0 repositories
ControlVAE: Tuning, Analytical Properties, and Performance Analysis
31 Oct 2020 · 4 repositories · arXiv:2011.01754

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.

Task	Papers
Disentanglement	2
Image Generation	2
Language Modeling	2
Language Modelling	2
Representation Learning	2

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (3 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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