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https://github.com/ziatdinovmax/semi-supervised-vae-nanoparticles

Semi-supervised VAE for data with the rotational disorder
https://github.com/ziatdinovmax/semi-supervised-vae-nanoparticles

image-recognition microscopy nanoparticles semi-supervised-learning vae

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Semi-supervised VAE for data with the rotational disorder

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# Semi-Supervised-VAE-nanoparticles

Codes (and data) for reproducing the results of the paper [**Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries, arXiv: 2105.11475**](https://arxiv.org/abs/2105.11475).

***ssrVAE_paper_synthetic.ipynb*** - This notebook demonstrates how to use a variational autoencoder (VAE) in a semi-supervised-setting to generalize from a small subset of labeled data with the weak orientational disorder to a large volume of unlabelled data with a much stronger orientational disorder. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/Semi-Supervised-VAE-nanoparticles/blob/main/ssrVAE_paper_synthetic.ipynb)

***ssrVAE_paper_particles.ipynb*** - Application of semi-supervised variational autoencoder (VAE) with rotational and translational invariances to scanning electron microscopy data from gold nanoparticles. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/Semi-Supervised-VAE-nanoparticles/blob/main/ssrVAE_paper_particles.ipynb)