@article{Bidart_Wong_2022, title={Disentangling Shape and Orientation with Affine Variational Autoencoders}, volume={7}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/4898}, DOI={10.15353/jcvis.v7i1.4898}, abstractNote={<p>Is it be possible to disentangle an object’s orientation from its shape? In this work we create compressed representations of an object by disentangling its orientation and shape with a variational autoencoder augmented with affine transform layers. Even when trained on randomly oriented data, shape and orientation are disentangled during training while the model learns to encode objects at a fixed orientation. We show this process results in a more compressed latent representation for 2d digits on the MNIST dataset, and for 3d objects on the ModelNet dataset.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Bidart, Rene and Wong, Alexander}, year={2022}, month={Apr.}, pages={1–3} }