Collaborative Pose Model Training With Remote Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Gathering high-quality training data and managing the processing power and memory requirements for training large neural networks pose significant challenges in machine learning model training.
Innovation Solution
A collaborative training system involving a local and remote computing device, where the local device generates a pose datum using a local machine learning model and a remote device refines it, allowing the local model to be retrained based on the refined datum, utilizing low-rank adaptation to reduce the number of trainable parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large neural network is used for pose estimation, then accuracy is improved, but processing power and memory requirements increase
Solution Approach 1:
The system segments the training and inference process between remote and local devices. The remote device performs computationally intensive model training and refinement, while the local device handles lightweight inference. This segmentation allows the local device to use a smaller model for pose estimation, reducing its processing power and memory requirements while maintaining accuracy through collaboration with the remote device.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on the remote device using large datasets and computational resources. The pre-trained model is then deployed to the local device, where it can perform pose estimation with reduced computational requirements. This preliminary training action enables the local device to achieve good accuracy without needing to process large amounts of data locally.
2Speed
If a local machine learning model is used for pose estimation, then processing speed is improved, but model accuracy may deteriorate without sufficient training data
Solution Approach 1:
The system implements feedback by having the remote device evaluate the local model's pose estimation results and generate refined training data based on performance gaps. The local device uses this feedback to iteratively improve its model accuracy through continued training, maintaining both speed and precision over time.
Solution Approach 2:
The local device benefits from preliminary action by receiving a pre-trained model from the remote device that has already been trained on extensive data. This head start allows the local model to achieve reasonable accuracy immediately, after which it can continue improving through localized fine-tuning with feedback from the remote device.
3Measurement precision
If high quality training data is gathered for specialized tasks, then model accuracy is improved, but data collection complexity increases
Solution Approach 1:
The system applies universality by using a general-purpose pre-trained model that can be adapted to specialized tasks through fine-tuning. Rather than collecting specialized training data for each specific task, the system uses a universal model foundation that can be refined for different applications, reducing data collection complexity while maintaining accuracy for specialized tasks.
Solution Approach 2:
The system performs preliminary action by pre-training a general model on diverse, general-purpose data before deployment. This preliminary training on broad data establishes a strong foundation that can be fine-tuned for specialized tasks with less additional data, reducing the complexity of gathering task-specific training data while achieving high accuracy.
Data Source
AI summary
Described herein is a system and a method for collaborative training of a machine learning model for pose estimation. The system includes a remote computing device in communication with a local computing device, wherein the remote computing device is configured to instruct the local computing device to receive a first image; and generate a first pose datum as a function of the first image using a local machine learning model. The remote computing device is configured to receive, from the local computing device, the first pose datum; generate a second pose datum as a function of the first pose datum and the first image using a remote machine learning model; and configure the local computing device to retrain the local machine learning model as a function of the second pose datum.