@inproceedings{
shewmake2023visual,
title={Visual Scene Representation with Hierarchical Equivariant Sparse Coding},
author={Christian A Shewmake and Domas Buracas and Hansen Lillemark and Jinho Shin and Erik J Bekkers and Nina Miolane and Bruno Olshausen},
booktitle={NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations},
year={2023},
url={https://openreview.net/forum?id=TF2RcrcTP2}
}
We propose a hierarchical neural network architecture for unsupervised learning of equivariant part-whole decompositions of visual scenes. In contrast to the global equivariance of group-equivariant networks, the proposed architecture exhibits equivariance to part-whole transformations throughout the hierarchy, which we term hierarchical equivariance. The model achieves such internal representations via hierarchical Bayesian inference, which gives rise to rich bottom-up, top-down, and lateral information flows, hypothesized to underlie the mechanisms of perceptual inference in visual cortex. We demonstrate these useful properties of the model on a simple dataset of scenes with multiple objects under independent rotations and translations.
Visual Scene Representation with Hierarchical Equivariant Sparse Coding
Christian A Shewmake Domas Buracas Hansen Lillemark Jinho Shin Erik J Bekkers Nina Miolane Bruno Olshausen