Joint Optimizer for Multi-Paradigm Computer Vision Training
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Solution Overview
Problem
Current deep learning neural network methods for computer vision applications often rely on a single machine learning technique and loss function, limiting their accuracy and flexibility in handling various computer vision tasks.
Innovation Solution
A multiple learning strategy framework that combines supervised, unsupervised, and weakly supervised learning techniques to train computer vision systems, using a joint optimizer to optimize multiple loss functions simultaneously, allowing for improved accuracy and flexibility across different computer vision applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning technique and loss function are used to train the computer vision system, then the training process is simple and computationally efficient, but the accuracy and performance of the system are limited
Solution Approach 1:
The training framework is segmented into three distinct learning modules: supervised learning system, unsupervised learning system, and weakly supervised learning system. Each module processes different types of loss functions independently before combining them through a joint optimizer, allowing the system to leverage multiple learning paradigms while maintaining modular architecture that manages complexity.
Solution Approach 2:
The patent combines multiple learning techniques (supervised, unsupervised, and weakly supervised) into a unified training framework. The joint optimizer merges the outputs of three separate learning systems, integrating their respective loss functions to achieve improved accuracy while systematically managing the complexity through structured combination rather than chaotic integration.
2Adaptability or versatility
If multiple loss functions are optimized simultaneously, then the system can achieve higher accuracy across different computer vision tasks, but the computational cost and training complexity increase
Solution Approach 1:
The joint optimizer is designed with multi-functionality to handle three different types of loss functions (supervised, unsupervised, and weakly supervised) within a single optimization framework. This universal optimizer can adapt to different computer vision tasks and data types, providing flexibility across monocular depth estimation, stereo matching, image enhancement, and multi-view depth estimation without requiring separate optimization systems for each task.
Solution Approach 2:
The training framework employs dynamic adjustment capabilities where the joint optimizer can adaptively balance the contributions of different loss functions during training. The system dynamically weights and combines losses from multiple learning systems, allowing flexible optimization that responds to the specific requirements of different computer vision applications and data characteristics.
Data Source
AI summary
A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.