5 citations · 8 across the 3 of their papers we have counts for
4 papers
Teaching Compositionality to CNNs
Austin Stone, Huayan Wang, Michael Stark +3
Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss…
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
Xinghua Lou, Ken Kansky, Wolfgang Lehrach +4
We demonstrate that a generative model for object shapes can achieve state of the art results on challenging scene text recognition tasks, and with orders of magnitude fewer traini…
A backward pass through a CNN using a generative model of its activations
Huayan Wang, Anna Chen, Yi Liu +2
Neural networks have shown to be a practical way of building a very complex mapping between a pre-specified input space and output space. For example, a convolutional neural networ…
Hierarchical compositional feature learning
Miguel Lázaro-Gredilla, Yi Liu, D. Scott Phoenix +1
We introduce the hierarchical compositional network (HCN), a directed generative model able to discover and disentangle, without supervision, the building blocks of a set of binary…