GAN-Based Image Classification With Limited Labeled Training Data
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Solution Overview
Problem
Existing machine learning models require large amounts of accurately labeled training data, which is time-consuming and costly to obtain, especially when the training data varies significantly with respect to conditions unrelated to the classification task, limiting their effectiveness in applications like crop phenotyping.
Innovation Solution
A method using a generative adversarial network (GAN) to leverage both labeled and unlabeled data, where a discriminator model is trained through supervised and unsupervised processes to learn the underlying structure of the input data, reducing the need for labeled examples by using a multi-step training approach that updates weights based on classification and real/fake image discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of accurately labeled training data are used to train machine learning models, then classification accuracy is improved, but time and cost to obtain training data increase significantly
Solution Approach 1:
The patent uses a generative model to create synthetic labeled images that copy the characteristics of real labeled images. These synthetic images are then used to train the classification model, reducing the need to obtain actual labeled data from crop images. The generative model learns from a small set of real labeled images and produces numerous synthetic variations that maintain the labeling accuracy while eliminating the time-consuming process of manual annotation.
Solution Approach 2:
The patent replaces expensive and time-consuming manual labeling processes with automated synthetic image generation. Instead of investing significant resources in obtaining high-quality labeled data, the system uses computationally generated images that can be produced rapidly and at minimal cost, achieving sufficient training data volume without the associated time and financial penalties.
2Measurement precision
If large amounts of accurately labeled training data are used to train machine learning models, then classification accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The generative model creates synthetic training images by copying and transforming existing labeled images through learned patterns. This approach requires computational resources for image synthesis rather than for processing large volumes of real data, which is more computationally efficient. The synthetic images provide sufficient training material without the need to store, process, and manage extensive datasets of actual crop images.
Solution Approach 2:
The patent extracts the essential labeling information from a small set of real labeled images and uses it to generate synthetic training data. By separating the valuable labeled information from the need for large volumes of real images, the system reduces computational costs associated with processing and storing extensive training datasets while maintaining classification accuracy through the extracted and replicated label characteristics.
3Quantity of substance
If transfer learning with unlabeled data is used, then availability of training data is improved, but model performance deteriorates when proportion of labeled data is very small
Solution Approach 1:
The generative model creates synthetic labeled images that copy the characteristics of real labeled images. These synthetic images are then used to train the classification model, reducing the need for actual labeled data. The generator learns from a small set of real labeled images and produces numerous synthetic variations that maintain the labeling accuracy while eliminating the time-consuming process of manual annotation.
Solution Approach 2:
The patent employs a feedback mechanism where the classification model's predictions on synthetic images are used to refine the generative model. This iterative process ensures that the synthetic images increasingly resemble the real labeled data distribution, improving model performance even when the initial proportion of labeled data is very small. The feedback loop continuously adjusts the synthetic image generation to maintain high training data quality.
Data Source
AI summary
Large amounts of high-accuracy annotated data are generally required to train a machine learning model to accurately classify input images. These requirements are significantly increased when the distribution of the images vary significantly with respect to factors like lighting, cultivar strain, or other conditions that are irrelevant to the factor of interest to be classified. Embodiments described herein employ generative adversarial networks to bootstrap a large amount of unlabeled images of a target (e.g., flowering plant) to learn the “classification irrelevant” aspects of the distribution of input images, allowing significantly smaller numbers of accurately labeled images to be used to obtain desired levels of classification accuracy. These embodiments can be used to identify flowering status in images of plants, or to identify other states in other subjects of interest where images thereof may represent significant factors (e.g., lighting, weather) that are not relevant to the classification task.