Attention-on-Label Training for Neural Network Image Annotations
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Solution Overview
Problem
Selecting labels for training images to train a neural network is resource-intensive in terms of memory, time, and computing resources.
Innovation Solution
Implementing an attention-on-label training scheme that utilizes multiple NLP algorithms as auto-labelers to select the most reliable annotation from various label sets for training a neural network, particularly for multi-label classification tasks, by processing training data with multiple noisy label sets to produce an accurate and robust model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional label selection methods are used to train a neural network, then the model can be trained with available annotations, but significant memory, time, and computing resources are consumed
Solution Approach 1:
The system performs preliminary action by using a pre-trained teacher network to generate pseudo-labels for all training images before the actual student network training begins. This advance preparation of labels eliminates the need for resource-intensive label selection during the student network training phase, significantly reducing training time and computational resources while maintaining training accuracy.
Solution Approach 2:
The teacher network serves as an intermediary between the available noisy annotations and the student network training process. It processes the noisy labels and generates cleaned pseudo-labels, acting as a mediator that filters out noise and provides high-quality training labels without requiring manual curation or resource-intensive selection processes.
2Reliability
If multiple noisy label sets are processed to improve model robustness, then the model accuracy improves, but computing resources and memory usage increase significantly
Solution Approach 1:
The teacher network performs preliminary processing of multiple noisy label sets to generate consolidated pseudo-labels before student network training. This advance processing consolidates information from multiple label sets into unified training targets, enabling the student network to learn from diverse annotations without the computational burden of processing each label set separately during training.
Solution Approach 2:
The system uses self-service by having the teacher network automatically generate high-quality pseudo-labels from noisy annotations without requiring external manual labeling or complex label selection algorithms. This self-labeling capability allows the system to leverage multiple label sets for improved robustness while avoiding the resource-intensive processes of manual verification or sophisticated label filtering.
3Productivity
If all available annotations are used for training, then the model can leverage maximum data, but the training process consumes excessive memory and computing resources
Solution Approach 1:
The system extracts only the essential labeling information needed for training by using the teacher network to generate consolidated pseudo-labels. Instead of loading and processing all available annotations simultaneously in memory, the teacher network extracts key label assignments and provides them to the student network, significantly reducing memory requirements while maintaining training effectiveness.
Solution Approach 2:
The approach changes the parameter of label representation from raw noisy annotations to processed pseudo-labels with confidence scores. This parameter transformation compresses the label information into a more efficient format that requires less memory storage and processing, enabling the system to leverage maximum training data without excessive resource consumption.
Data Source
AI summary
Apparatuses, systems, and techniques to select labels of training images to train a network. In at least one embodiment, one or more labels of training images are selected to train a network.