Multi-Sensor ML Training with Zero-Loss Regions
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Solution Overview
Problem
Current machine-learning model training, particularly for deep-learning systems, faces challenges in collecting and labeling large volumes of accurate audio data due to the unlimited variations in environmental sounds, leading to costly and time-consuming manual annotation processes, and the use of low-quality automatically labeled data.
Innovation Solution
Implementing a system that utilizes multiple ground-truth sensing systems, such as cameras and radar, in conjunction with prediction sensing systems like audio sensors to automatically collect and label data, defining loss and correctness functions to train machine learning models, thereby reducing the effort required for dataset generation and improving precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation processes are used to label audio data, then data accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent uses multiple ground-truth sensing systems (camera, radar, photocell) to create alternative representations or 'copies' of the same physical reality. These visual/sensor copies are then used to automatically generate audio labels without manual annotation, resolving the contradiction between accuracy and time consumption by replacing human labor with automated sensor-based copying and translation processes.
Solution Approach 2:
The patent introduces an intermediary machine learning model that translates data from ground-truth sensing systems into audio labels. This intermediary automatically generates accurate labels by learning the relationship between sensor data and audio characteristics, eliminating the need for time-consuming manual annotation while maintaining high data accuracy.
2Measurement precision
If multiple ground-truth sensing systems are used to collect data, then data accuracy is improved, but device complexity increases
Solution Approach 1:
The patent makes the machine learning model multi-functional by enabling it to process and translate data from multiple different sensing systems (camera, radar, photocell). This universal model handles various sensor types through a single unified architecture, reducing the need for separate processing systems for each sensor type and thereby managing complexity while maintaining high data accuracy.
Solution Approach 2:
The patent changes the parameter space by transforming data from different sensor modalities into a unified representation that the machine learning model can process. By converting diverse sensor inputs into a common parameter format, the system manages complexity while充分利用 the accuracy benefits of multiple sensing systems.
3Reliability
If large volumes of training data are collected to improve model performance, then model accuracy is improved, but data collection effort and cost increase
Solution Approach 1:
The system implements self-service by enabling automatic generation of labeled training data through the machine learning model that translates ground-truth sensor data into audio labels. This self-labeling capability eliminates the need for external manual annotation services, allowing the system to generate large volumes of high-quality training data efficiently and autonomously, thereby improving both model accuracy and data collection productivity.
Data Source
AI summary
A method of training a machine learning (ML) model includes obtaining a dataset that includes first training data obtained using two or more ground truth sensing systems and second training data obtained using a prediction sensing system configured to implement the ML model, determining a loss function based on the first training data, the loss function defining a region of zero loss based on a minimum and a maximum of the first training data, calculating, using the ML model, a prediction output based on the second training data, calculating, using the loss function, a loss of the ML model based on the prediction output, and updating the ML model based on the calculated loss.