Learning Silhouettes with Group Sparse Autoencoders

Sparse coding has been extensively used in neuroscience to model brain-like computation by drawing analogues between neurons’ firing activity and the nonzero elements of sparse vectors. Contemporary deep learning architectures have been used to model neural activity, inspired by signal processing algorithms; however sparse coding architectures are not able to explain the higher-order categorization that has been empirically observed at the neural level. In this work, we propose a novel model-based architecture, termed group-sparse autoencoder, that produces sparse activity patterns in line with neural modeling, but showcases a higher-level order in its activation maps. We evaluate a dense model of our architecture on MNIST and CIFAR-10 and show that it learns dictionaries that resemble silhouettes of the given class, while its activations have a significantly higher level order compared to sparse architectures. Source code is available at: https://github.com/manosth/silhouette-learning.