Neural Network Patch Selection for Efficient End-to-End Image Training
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Solution Overview
Problem
Existing neural network training methods require processing the entire image using computationally-expensive subnetworks without knowing which portions are relevant for the prediction, leading to inefficiencies in computational, time, and memory usage, and result in less accurate parameter updates due to approximated gradients.
Innovation Solution
A neural network training system that generates relevance scores for image patches using a computationally-inexpensive scoring subnetwork, selects the highest scores with differentiable operations, and processes these patches with more expensive subnetworks to generate predictions, allowing for accurate backpropagation of errors and efficient training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire image is processed using computationally-expensive subnetworks, then prediction accuracy is improved, but computational efficiency and training time deteriorate
Solution Approach 1:
The image is divided into multiple patches, and a scoring subnetwork assigns relevance scores to each patch. Only the top-k most relevant patches are selected for processing by the expensive subnetworks, thereby segmenting the computational workload and improving efficiency while maintaining accuracy.
Solution Approach 2:
Different parts of the image (patches) are treated differently based on their relevance scores. High-relevance patches undergo expensive processing with accurate subnetworks, while low-relevance patches are either processed with simpler methods or discarded, applying local quality differentiation to optimize the trade-off between accuracy and efficiency.
2Measurement precision
If the entire image is processed using computationally-expensive subnetworks, then prediction accuracy is improved, but memory usage deteriorates
Solution Approach 1:
The method extracts only the most relevant image patches based on relevance scores, removing unnecessary patches from further processing. This extraction reduces the quantity of data that needs to be held in memory during expensive subnetwork processing, thereby reducing memory usage while preserving accuracy.
3Productivity
If non-differentiable operations are used to select highest relevance scores, then computational efficiency is improved, but gradient accuracy deteriorates
Solution Approach 1:
A differentiable selection mechanism is introduced as an intermediary between the relevance scoring and patch processing stages. This intermediary uses differentiable operations (such as soft selection or differentiable top-k) to select patches while maintaining gradient flow, ensuring that gradient accuracy is preserved despite the selection process.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to process images. One of the methods includes obtaining a training image; processing the training image using a first subnetwork to generate, for each of a plurality of first image patches of the training image, a relevance score; generating, using the relevance scores, one or more second image patches of the training image by performing one or more differentiable operations on the relevance scores; processing the one or more second image patches using a second subnetwork to generate a prediction about the training image; determining an error of the training network output; and generating a parameter update for the first subnetwork, comprising backpropagating gradients determined according to the error of the training network output through i) the second subnetwork, ii) the one or more differentiable operations, and iii) the first subnetwork.