Neural Network Variable Resolution Training
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Solution Overview
Problem
Existing techniques for using neural networks, such as for image generation, require significant resources and are inefficient, particularly when training with higher resolution images, which are costly and time-consuming.
Innovation Solution
The system adjusts the resolution of information used by neural networks based on performance metrics, progressively training from low to higher resolutions, reducing resource expenditure while achieving similar model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained with high resolution images, then model accuracy is improved, but computing resources and training time increase significantly
Solution Approach 1:
The patent segments the training process into multiple stages with progressively increasing resolution. The training dataset is divided into different resolution groups (e.g., low resolution, medium resolution, high resolution), and the neural network trains sequentially through these groups rather than using all high-resolution images from the start. This segmentation allows the model to learn basic patterns efficiently at lower resolutions before refining details at higher resolutions, thereby reducing overall computing resource consumption while maintaining model accuracy.
2Measurement precision
If neural networks are trained with high resolution images, then model accuracy is improved, but training time increases significantly
Solution Approach 1:
The patent applies preliminary action by first training the neural network on low-resolution images to establish foundational pattern recognition capabilities before introducing high-resolution images. This preliminary training phase allows the model to converge faster when high-resolution images are subsequently introduced, as the network already has learned basic features and structures. This staged approach prevents the model from wasting time processing excessive detail too early in the training process.
3Manufacturing precision
If resolution of training data is increased, then image generation quality is improved, but resource expenditure increases
Solution Approach 1:
The patent implements local quality by applying different resolution levels to different portions of the training process and different regions of the image data. Rather than uniformly processing all images at maximum resolution, the system uses lower resolutions for images where fine detail is less critical and higher resolutions for images where detail is important. This localized approach to quality optimization maintains overall image generation quality while reducing total resource expenditure on processing.
Data Source
AI summary
Apparatuses, systems, and techniques to use one or more neural networks with adjusted resolution information. In at least one embodiment, for example, one or more neural network low resolution encoders are trained to one or more higher resolutions. In at least one embodiment, as another example, a processor is to adjust a resolution of information to be used by one or more neural networks based, at least in part, on one or more performance metrics of one or more neural networks.