26 citations · 73 across the 7 of their papers we have counts for
5 papers · 1 filter
Neural Image Decompression: Learning to Render Better Image Previews
Shumeet Baluja, Dave Marwood, Nick Johnston +1
A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited- and metered-bandwidth constraints. To satisfy these requests, it has bec…
No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference
Shumeet Baluja, David Marwood, Michele Covell +1
For successful deployment of deep neural networks on highly--resource-constrained devices (hearing aids, earbuds, wearables), we must simplify the types of operations and the memor…
Towards a Semantic Perceptual Image Metric
Troy Chinen, Johannes Ballé, Chunhui Gu +8
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification. We fit the metric to a new database based on 14…
Spatially adaptive image compression using a tiled deep network
David Minnen, George Toderici, Michele Covell +6
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images. Existing image compression algorithms based on neural n…
Variational image compression with a scale hyperprior
Johannes Ballé, David Minnen, Saurabh Singh +2
We describe an end-to-end trainable model for image compression based on variational autoencoders. The model incorporates a hyperprior to effectively capture spatial dependencies i…