Multi-exit visual synthesis network based on dynamic patch computing
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Solution Overview
Problem
Existing visual synthesis networks, such as image super-resolution on GPUs, face scalability issues due to reliance on pixel-wise sparse convolution, which is not hardware-friendly and achieves limited practical speedup.
Innovation Solution
A multi-exit visual synthesis network (VSN) based on dynamic patch computing (DPC) that splits input images into patches, applies patch-wise sparse convolution, and adaptively adjusts the number of layers for each patch, using a regressor to predict incremental improvements and determine early exits based on thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pixel-wise sparse convolution is used to achieve scalability, then the network can be deployed on platforms with different capacities, but the practical speedup is limited and it is not hardware-friendly
Solution Approach 1:
The patent divides the image into multiple patches and processes each patch independently through the network. This segmentation allows the computational workload to be distributed and parallelized across multiple processing units, making the system scalable while achieving practical speedup since each patch can be processed efficiently using standard convolution operations rather than complex pixel-wise sparse convolution
Solution Approach 2:
The patent introduces dynamic early exit mechanisms that allow different patches to exit the network at different stages based on their complexity and reconstruction quality. This dynamic adaptation enables the system to scale efficiently across different hardware capacities by adjusting the number of processing stages per patch, achieving both scalability and practical speedup without requiring hardware-unfriendly sparse convolution
2Manufacturing precision
If the number of layers is increased to maintain accuracy, then the synthesis quality improves, but the computational cost increases
Solution Approach 1:
The patent implements dynamic early exit mechanisms where patches can leave the network at different stages based on their reconstruction quality and complexity. Simple patches exit early with fewer computational operations, while complex patches continue through more layers to maintain accuracy. This dynamic approach maintains high synthesis accuracy for difficult patches while reducing overall computational cost across the entire image
Solution Approach 2:
The patent applies different processing depths to different patches based on their local characteristics and reconstruction needs. Rather than uniformly processing all patches through the same number of layers, the system adapts the computational depth locally to each patch's requirements, maintaining accuracy where needed while reducing computational cost in simpler regions
3Manufacturing precision
If the network processes all patches through all layers, then the synthesis accuracy is maximized, but the processing time increases
Solution Approach 1:
The patent introduces dynamic early exit mechanisms that allow patches to leave the network at different stages based on their reconstruction quality. This dynamic processing reduces the average number of layers processed per patch, significantly decreasing processing time while maintaining synthesis accuracy for patches that require full processing
Solution Approach 2:
The patent applies partial processing to patches that achieve sufficient reconstruction quality before completing all layers. By allowing early exit when accuracy thresholds are met, the system performs only the necessary computational action required for each patch, reducing overall processing time while maintaining required synthesis accuracy
Data Source
AI summary
The application relates to a multi-exit visual synthesis network (VSN) based on dynamic patch computing. A method for visual synthesis is provided and includes: splitting an input image into multiple input patches; performing a synthesis process on each input patch with a first layer to an ith exit layer of a multi-exit VSN to obtain an ith intermediate synthesis patch, where i is an index of an intermediate exit of the VSN and predetermined as an integer greater than or equal to 1; predicting an incremental improvement of a (i+1)th intermediate synthesis patch relative to the ith intermediate synthesis patch based on features in the ith intermediate synthesis patch; determining a final exit of the VSN and a final synthesis patch for the input patch based on the predicted incremental improvement; and merging respective final synthesis patches for the multiple input patches to generate an output image.