10 citations · 10 across the 2 of their papers we have counts for
4 papers
MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning
Andrey Ignatov, Anastasia Sycheva, Radu Timofte +8
While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very…
PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks
Andrey Ignatov, Grigory Malivenko, Radu Timofte +8
The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile…
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks
Po-Hsiang Yu, Sih-Sian Wu, Liang-Gee Chen
Pruning has become a promising technique used to compress and accelerate neural networks. Existing methods are mainly evaluated on spare labeling applications. However, dense label…
Joint Pruning & Quantization for Extremely Sparse Neural Networks
Po-Hsiang Yu, Sih-Sian Wu, Jan P. Klopp +2
We investigate pruning and quantization for deep neural networks. Our goal is to achieve extremely high sparsity for quantized networks to enable implementation on low cost and low…