Invertible Wavelet Layer for Neural Network Resolution
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Solution Overview
Problem
Deep neural networks face significant computational waste due to the lossy nature of pooling operations, which discard a large portion of previously computed values, and the subsequent upsampling process introduces redundancies, leading to lower quality image restoration.
Innovation Solution
Implementing invertible wavelet layers that losslessly reduce and expand the resolution of feature maps, replacing traditional pooling and upsampling layers, allowing for the retention of computations and maintaining image quality through the use of wavelet and inverse wavelet transforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pooling layers are used to reduce resolution, then computational efficiency is improved, but information loss increases
Solution Approach 1:
The patent transforms the pooling operation from a lossy downsampling approach to a lossless wavelet transform approach. By changing the mathematical transformation parameters and using invertible wavelet transforms, the system maintains all original information while achieving resolution reduction, thereby eliminating information loss without sacrificing computational efficiency.
Solution Approach 2:
The patent replaces the traditional pooling mechanism (which discards information) with a wavelet transform mechanism (which preserves information). This substitution uses a different mathematical foundation - wavelet theory instead of simple pooling - to achieve the same resolution reduction goal without the harmful side effect of information loss.
2Manufacturing precision
If upsampling layers are used to restore resolution, then image restoration is achieved, but computation redundancies increase
Solution Approach 1:
The patent inverts the traditional approach by using invertible wavelet transforms that can seamlessly transition between downsampling and upsampling. Instead of using separate pooling and upsampling operations that create redundancies, the system uses a unified wavelet transform framework where the inverse operation perfectly reconstructs the original signal, eliminating computation redundancies while maintaining restoration quality.
3Productivity
If traditional pooling and upsampling operations are used, then resolution reduction and restoration are achieved, but computational waste increases
Solution Approach 1:
The patent creates a universal wavelet transform layer that serves multiple functions: resolution reduction, information preservation, and lossless reconstruction. This multi-functional approach replaces the need for separate pooling and upsampling operations, consolidating their functions into a single efficient operation that eliminates computational waste while maintaining all desired capabilities.
Data Source
AI summary
An electronic device, method, and computer readable medium for an invertible wavelet layer for neural networks are provided. The electronic device includes a memory and at least one processor coupled to the memory. The at least one processor is configured to receive an input to a neural network, apply a wavelet transform to the input at a wavelet layer of the neural network, and generate a plurality of subbands of the input as a result of the wavelet transform.