26 citations · 38 across the 3 of their papers we have counts for
9 papers
Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression
A. Murat Tekalp, Michele Covell, Radu Timofte +1
Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods. Hence, the stat…
Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference
Michele Covell, David Marwood, Shumeet Baluja +1
In this work, we propose to quantize all parts of standard classification networks and replace the activation-weight--multiply step with a simple table-based lookup. This approach…
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…
Representing Images in 200 Bytes: Compression via Triangulation
David Marwood, Pascal Massimino, Michele Covell +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 beco…
Image-Dependent Local Entropy Models for Learned Image Compression
David Minnen, George Toderici, Saurabh Singh +2
The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that are optimized for rate-distortion…