Visual Labeling for Machine Learning Training Data
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Solution Overview
Problem
Existing machine learning model training methods face inefficiencies due to the separation of training data and labels, which can lead to inconvenient data transfer and manual labeling costs, especially when dealing with altered data items like blurred images, and do not support robust datasets for imperfect data classification.
Innovation Solution
The method involves automatically generating and embedding visual labels, such as QR codes, directly into training data items within the same image, using a separator to distinguish between the data and label, allowing for automatic labeling and comparison during model training, validation, and testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If labels are stored separately from training data items, then data transfer becomes inconvenient and files may become separated, but embedding labels within the same file increases file complexity and may interfere with data processing
Solution Approach 1:
The patent combines the training data item and its corresponding label into a single image file, where the label is embedded as a visual element (such as a QR code or text overlay) within the same image. This merging eliminates the need for separate label files, ensures labels remain attached to their corresponding data during transfer, and simplifies the file structure while maintaining ease of operation.
2Adaptability or versatility
If metadata is used to store labels, then labels can be embedded in the file, but metadata is not supported by all visual formats and may change when the training data item is altered
Solution Approach 1:
The patent uses visual characteristics such as color, position, or other perceptible features of the embedded label to encode the classification information. This approach ensures the label is embedded directly in the image data in a way that is supported by all visual formats and remains consistent even when the training data item is altered, as the label is rendered as part of the image itself rather than stored in mutable metadata fields.
3Measurement precision
If training data items are manually labeled, then accurate labels can be assigned, but substantial time and cost investment is required that increases with data complexity and size
Solution Approach 1:
The patent implements self-service labeling by training a machine learning model to automatically generate labels for training data items. The model processes each image and assigns classifications automatically, eliminating the need for manual labeling. This approach maintains label accuracy while dramatically improving productivity, as the automated system can process large volumes of data quickly and consistently without human intervention.
4Adaptability or versatility
If training data items are altered (such as blurring), then the model can learn to classify imperfect data, but separate label files prevent creation of robust datasets with alterations
Solution Approach 1:
By embedding the label directly within the image file, the patent enables the creation of robust datasets where altered training data items (such as blurred, rotated, or cropped images) can be easily combined with their corresponding labels in a single file. This merging allows for flexible data augmentation and transformation while maintaining the association between the altered data and its label, greatly simplifying dataset creation and improving data robustness for training.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for visual labeling of training data items for training a machine learning model. Training data items may be generated for training the machine learning model. Visual labels, such as QR codes, may be created for the training data items. The creation of the training data item and the visual label may be automated. The visual labels and the training data items may be combined to obtain a labeled training data item. The labeled training data item may comprise a separator to distinguish the training data item from the visual label. The labeled training data item may be used for training and validation of the machine learning model. The machine learning model may analyze the training data item, attempt to identify the training data item, and compare the identification against the embedded label.