Search Results for author: D. Scott Phoenix

Found 4 papers, 0 papers with code

Teaching Compositionality to CNNs

no code implementations CVPR 2017 Austin Stone, Huayan Wang, Michael Stark, Yi Liu, D. Scott Phoenix, Dileep George

Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss functions.

Object Recognition

Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data

no code implementations NeurIPS 2016 Xinghua Lou, Ken Kansky, Wolfgang Lehrach, CC Laan, Bhaskara Marthi, D. Scott Phoenix, Dileep George

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 training images than required for competing discriminative methods.

Instance Segmentation Scene Text Recognition +1

A backward pass through a CNN using a generative model of its activations

no code implementations8 Nov 2016 Huayan Wang, Anna Chen, Yi Liu, Dileep George, D. Scott Phoenix

Neural networks have shown to be a practical way of building a very complex mapping between a pre-specified input space and output space.

Hierarchical compositional feature learning

no code implementations7 Nov 2016 Miguel Lázaro-Gredilla, Yi Liu, D. Scott Phoenix, Dileep George

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 images.

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