Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

• URL: http://arxiv.org/abs/2105.11475v1
• Date: Mon, 24 May 2021 18:01:57 GMT
• Title: Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries
• Authors: Maxim Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David Ginger, and Sergei V. Kalinin
• Abstract summary: In most cases, experimental data streams contain images having arbitrary rotations and translations within the image. We develop an approach that allows generalizing from a small subset of labeled data to a large unlabeled dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of optical, electron, and scanning probe microscopies gives rise to large volumes of imaging data of objects as diversified as cells, bacteria, pollen, to nanoparticles and atoms and molecules. In most cases, the experimental data streams contain images having arbitrary rotations and translations within the image. At the same time, for many cases, small amounts of labeled data are available in the form of prior published results, image collections, and catalogs, or even theoretical models. Here we develop an approach that allows generalizing from a small subset of labeled data with a weak orientational disorder to a large unlabeled dataset with a much stronger orientational (and positional) disorder, i.e., it performs a classification of image data given a small number of examples even in the presence of a distribution shift between the labeled and unlabeled parts. This approach is based on the semi-supervised rotationally invariant variational autoencoder (ss-rVAE) model consisting of the encoder-decoder "block" that learns a rotationally (and translationally) invariant continuous latent representation of data and a classifier that encodes data into a finite number of discrete classes. The classifier part of the trained ss-rVAE inherits the rotational (and translational) invariances and can be deployed independently of the other parts of the model. The performance of the ss-rVAE is illustrated using the synthetic data sets with known factors of variation. We further demonstrate its application for experimental data sets of nanoparticles, creating nanoparticle libraries and disentangling the representations defining the physical factors of variation in the data. The code reproducing the results is available at https://github.com/ziatdinovmax/Semi-Supervised-VAE-nanoparticles.

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