Monocular Depth Model Selection Using Small DNN Proxies
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Solution Overview
Problem
Large deep neural networks (DNNs) are inefficient due to high memory footprint, resource consumption, and long training times, making them unsuitable for real-time operations and requiring extensive datasets, despite achieving high accuracy.
Innovation Solution
Implementing multiple small DNNs, each trained for specific subsets of input data, and selecting the most efficient model based on accuracy and loss comparison with a large DNN's output, allowing for efficient processing of diverse datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large deep neural network is used, then accuracy is improved, but resource consumption and training time increase
Solution Approach 1:
The patent divides the large model selection problem into segments by creating multiple smaller models with different architectures and training them on different subsets of the dataset. This allows parallel training of multiple small models instead of training one large model, significantly reducing training time while maintaining competitive accuracy through ensemble selection.
Solution Approach 2:
The patent uses multiple small, computationally inexpensive models that can be quickly trained and discarded for model selection purposes. These small models serve as proxies to evaluate which model architecture works best for a given dataset, avoiding the need to train expensive large models multiple times.
2Measurement precision
If a large deep neural network is used, then accuracy is improved, but memory footprint increases
Solution Approach 1:
The patent employs multiple small models with reduced memory footprints instead of a single large model. These small models are trained on dataset subsets and used to determine the optimal model architecture, thereby minimizing memory requirements while still achieving accurate predictions through the selected model.
Solution Approach 2:
The patent changes model parameters by creating small models with different architectures, layer configurations, and hyperparameters. By varying these parameters across multiple small models, the system identifies the optimal configuration for the given dataset without requiring a large model, thus reducing memory footprint.
3Measurement precision
If a large deep neural network is used, then accuracy is improved, but processing resources increase
Solution Approach 1:
The patent segments the computational workload by dividing it into training multiple small models on different data subsets rather than training one large model. This segmentation enables parallel processing of small models, reducing overall processing resource consumption and energy usage while maintaining accuracy through model selection.
Solution Approach 2:
The patent uses computationally cheap small models for model selection and evaluation purposes. These small models consume minimal processing resources and can be quickly trained and evaluated, allowing the system to identify the best model architecture without expending significant computational energy.
4Measurement precision
If a large deep neural network is used, then accuracy is improved, but model complexity increases
Solution Approach 1:
The patent segments the model complexity by creating multiple simple small models with different architectural characteristics rather than using one complex large model. Each small model has reduced complexity, but collectively they cover a diverse range of architectural possibilities, allowing the system to select the simplest effective model for the given dataset.
Data Source
AI summary
Systems and methods are described for selecting models to perform monocular depth estimation. A computing device may receive a plurality of image and select an image from the plurality of images to evaluate a first machine-learning model and a plurality of machine-learning models are smaller than the first machine-learning model. The computing device may process the image using a first machine-learning model to generate a first predicted result. The computing device may also process the image using the plurality of machine-learning models to generate at least a second predicted result and a third test data set. The computing device may select a second machine-learning model from the plurality of machine-learning models based on a comparison of the first predicted result with the at least the second predicted result and the third test data set. The computing device may then process the plurality of images using the second machine-learning model.