Posts by Collection

projects

Malware Detection by Eating a Whole EXE

Published:

This paper shows how to do whole binary classification for malware detection with a convolutional neural network. Done in collaboration with researchers at the University of Maryland.

Recommended citation: Edward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, Charles Nicholas, Malware Detection by Eating a Whole EXE. arXiv. 2017. http://arxiv.org/abs/1710.09435

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Published:

This paper shows how to do large scale distributed, large batch, mixed precision training of language models with investigations into the successes and limitations of large batch training on publicly available language datasets.

Recommended citation: Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro, Large Scale Language Modeling: Converging on 40GB of Text in Four Hours. arXiv. 2018. https://arxiv.org/abs/1808.01371

SDCNet: Video Prediction Using Spatially Displaced Convolution

Published:

SDCNet is a 3D convolutional neural network proposed for frame prediction. The model takes as input a sequence of past frames and their inter-frame optical flows and generates a per-pixel kernel and motion vector. A future frame is then synthesised by sampling past frames guided by the motion vectors and weighted by the learned kernels.

Recommended citation: Fitsum A. Reda, Guilin Liu, Kevin J. Shih, Robert Kirby, Jon Barker, David Tarjan, Andrew Tao, Bryan Catanzaro, SDCNet: Video Prediction Using Spatially Displaced Convolution. ECCV 2018. https://arxiv.org/abs/1811.00684

Improving Semantic Segmentation via Video Propagation and Label Relaxation

Published:

This paper shows how to scale up training sets for semantic segmentation by using video prediction-based data synthesis method. Our proposed joint propagation strategy and boundary relaxation technique can alleviate the label noise in the synthesized samples and lead to state-of-the-art performance on three benchmark datasets Cityscapes, CamVid and KITTI.

Recommended citation: Yi Zhu, Karan Sapra, Fitsum A. Reda, Kevin J. Shih, Shawn Newsam, Andrew Tao and Bryan Catanzaro, Improving Semantic Segmentation via Video Propagation and Label Relaxation, arXiv:1812.01593, 2018. https://arxiv.org/abs/1812.01593