# β-Annealed Variational Autoencoder for glitches

arXiv: https://arxiv.org/abs/2107.10667 · HTML: https://mlsanity.com/paper/2107.10667/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [Disentangling by Factorising](https://mlsanity.com/paper/1802.05983/) (2018)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Variational Inference of Disentangled Latent Concepts from Unlabeled Observations](https://mlsanity.com/paper/1711.00848/) (2017)
- [Learning Deep Disentangled Embeddings with the F-Statistic Loss](https://mlsanity.com/paper/1802.05312/) (2018)
- [Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO](https://mlsanity.com/paper/1706.07446/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run](https://mlsanity.com/paper/2412.19883/) (2024)

## Abstract (arXiv)

Gravitational wave detectors such as LIGO and Virgo are susceptible to various types of instrumental and environmental disturbances known as glitches which can mask and mimic gravitational waves. While there are 22 classes of non-Gaussian noise gradients currently identified, the number of classes is likely to increase as these detectors go through commissioning between observation runs. Since identification and labelling new noise gradients can be arduous and time-consuming, we propose β-Annelead VAEs to learn representations from spectograms in an unsupervised way. Using the same formulation as citealemi2017fixing, we view Bottleneck-VAEs~citeburgess2018understanding through the lens of information theory and connect them to β-VAEs~citehiggins2017beta. Motivated by this connection, we propose an annealing schedule for the hyperparameter β in β-VAEs which has advantages of: 1) One fewer hyperparameter to tune, 2) Better reconstruction quality, while producing similar levels of disentanglement.