# Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy

arXiv: https://arxiv.org/abs/2104.10207 · HTML: https://mlsanity.com/paper/2104.10207/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)

## Built on by (most cited first, 2 of 2)

- [Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders](https://mlsanity.com/paper/2303.18236/) (2023)
- [Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning](https://mlsanity.com/paper/2312.05160/) (2023)

## Abstract (arXiv)

A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables. Using synthetic data sets, we show that the shift-VAE latent variables closely match the ground truth parameters. The shift VAE is extended towards the analysis of band-excitation piezoresponse force microscopy (BE-PFM) data, disentangling the resonance frequency shifts from the peak shape parameters in a model-free unsupervised manner. The extensions of this approach towards denoising of data and model-free dimensionality reduction in imaging and spectroscopic data are further demonstrated. This approach is universal and can also be extended to analysis of X-ray diffraction, photoluminescence, Raman spectra, and other data sets.