10 citations · 17 across the 7 of their papers we have counts for
7 papers
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
Binxiao Huang, Jason Chun Lok Li, Jie Ran +4
Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require special…
Lite it fly: An All-Deformable-Butterfly Network
Rui Lin, Jason Chun Lok Li, Jiajun Zhou +3
Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matr…
PECAN: A Product-Quantized Content Addressable Memory Network
Jie Ran, Rui Lin, Jason Chun Lok Li +2
A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ). This results in a natural…
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
Rui Lin, Jie Ran, King Hung Chiu +2
We introduce a new kind of linear transform named Deformable Butterfly (DeBut) that generalizes the conventional butterfly matrices and can be adapted to various input-output dimen…
EZCrop: Energy-Zoned Channels for Robust Output Pruning
Rui Lin, Jie Ran, Dongpeng Wang +2
Recent results have revealed an interesting observation in a trained convolutional neural network (CNN), namely, the rank of a feature map channel matrix remains surprisingly const…
Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks
Jie Ran, Rui Lin, Hayden K. H. So +2
Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs). Recognizing that the kernels in a convolutional neural netwo…
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…