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Found 4 papers, 1 papers with code
MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning
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 limited due to their very high computational complexity.
PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks
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 camera sensor limitations.
KCP: Kernel Cluster Pruning for Dense Labeling Neural Networks
We developed a clustering technique to identify the least representational kernels in each layer.
Joint Pruning & Quantization for Extremely Sparse Neural Networks
We investigate pruning and quantization for deep neural networks.
