# Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction

arXiv: https://arxiv.org/abs/2101.03549 · HTML: https://mlsanity.com/paper/2101.03549/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 10 Jan 2021.

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

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [AttGAN: Facial Attribute Editing by Only Changing What You Want](https://mlsanity.com/paper/1711.10678/) (2017)
- [Explorations in Homeomorphic Variational Auto-Encoding](https://mlsanity.com/paper/1807.04689/) (2018)
- [Explicitly disentangling image content from translation and rotation with spatial-VAE](https://mlsanity.com/paper/1909.11663/) (2019)
- [Balancing Specialization, Generalization, and Compression for Detection and Tracking](https://mlsanity.com/paper/1909.11348/) (2019)
- [Computational Methods for Single-Particle Cryo-EM](https://mlsanity.com/paper/2003.13828/) (2020)

## Built on by (most cited first, 5 of 5)

- [Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection](https://mlsanity.com/paper/2110.09246/) (2021)
- [Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression](https://mlsanity.com/paper/2102.07181/) (2021)
- [Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness](https://mlsanity.com/paper/2109.01945/) (2021)
- [Collaborative Image Understanding](https://mlsanity.com/paper/2210.11907/) (2022)
- [Joint Denoising of Cryo-EM Projection Images using Polar Transformers](https://mlsanity.com/paper/2506.11283/) (2025)

## Abstract (arXiv)

Cryo-Electron Microscopy (Cryo-EM) is a Nobel prize-winning technology for determining the 3D structure of particles at near-atomic resolution. A fundamental step in the recovering of the 3D single-particle structure is to align its 2D projections; thus, the construction of a canonical representation with a fixed rotation angle is required. Most approaches use discrete clustering which fails to capture the continuous nature of image rotation, others suffer from low-quality image reconstruction. We propose a novel method that leverages the recent development in the generative adversarial networks. We introduce an encoder-decoder with a rotation angle classifier. In addition, we utilize a discriminator on the decoder output to minimize the reconstruction error. We demonstrate our approach with the Cryo-EM 5HDB and the rotated MNIST datasets showing substantial improvement over recent methods.