  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.
