Active Learning Neural Network for Image Labeling Efficiency
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Solution Overview
Problem
Training artificial neural networks for video and image processing using labelled images is cumbersome and expensive, as existing methods require extensive and costly labeling of datasets.
Innovation Solution
A data-driven method using a deep Bayesian Neural Net to estimate prediction uncertainty, combined with a probabilistic policy network trained via reinforcement feedback, to selectively label the most relevant images, reducing the need for extensive labeling through active learning and recursive training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive labelled images are used for training, then the performance of the neural network is improved, but the cost and time required for labeling increases
Solution Approach 1:
The system uses the neural network itself to identify which images would be most beneficial to label, making the selection process autonomous and eliminating the need for manual assessment of image usefulness
Solution Approach 2:
The system implements a feedback loop where the neural network's prediction uncertainty about selected images feeds back into the selection process, allowing the system to learn and improve its image selection strategy over multiple labeling rounds
2Measurement precision
If more images are labeled, then the training data quality is improved, but the cost of labeling increases
Solution Approach 1:
The neural network autonomously identifies high-value training images by evaluating its own prediction uncertainty, eliminating the need for expensive manual assessment of which images deserve labeling
Solution Approach 2:
The system changes the parameter of image selection from random or manual choice to uncertainty-based selection, where images are chosen based on the neural network's predicted uncertainty rather than arbitrary criteria
3Ease of operation
If a deterministic acquisition function is used, then the image selection process is simplified, but the ability to adapt to different data sets is reduced
Solution Approach 1:
The system transforms the static, deterministic acquisition function into a dynamic, adaptive process by using the neural network's predicted uncertainty as a variable that changes with each labeling round, allowing the selection strategy to evolve based on the data being processed
Solution Approach 2:
The neural network generates its own acquisition function by computing prediction uncertainty, eliminating the need for external deterministic functions and enabling automatic adaptation to different data sets
Data Source
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Figure 3
AI summary
A learning system (100) and a method of operating the learning system, wherein the learning system (100) comprises at least one processor and at least one memory for instructions that are executable by the processor, wherein the at least one processor is adapted to execute the instructions for selecting a representation (xn) of an image from a plurality of representations of images, determining a first representation (y) of at least one first label for the image, determining a second representation (yn) of at least on second label in a prediction, determining an uncertainty of the prediction of the second representation (yn) in particular based on the representation (xn) of the image, the first representation (y) and the second representation (yn), determining a reward (R) depending on the representation (xn) of the image and/or the second representation (yn), and selecting another representation of another image depending on the uncertainty and on the reward (R), wherein the system (100) is adapted to determine at least one control signal for a video-based or an image-based surveillance system, customer monitoring system, safety engineering system, driver assistance system, multifunctional comfort system, safety-critical assistance system with intervention in a vehicle control, system for melding of driver information or driver assistance, robotic system, household or garden work robot system, networked or cooperative robotic system, wherein the control signal is determined as output of the learning system (100) in response to an input of at least one image captured for the system (100), and wherein the system (100) is adapted to process images depend on a sensor output of an image, video, radar, LiDAR, ultrasonic, or motion sensor.