Dynamic Image Transformation for Active Learning Model Retraining
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Solution Overview
Problem
Existing machine learning models, particularly those using deep learning for computer vision tasks, require large volumes of annotated data for training, which is time-consuming and costly. Additionally, current active learning methods employ fixed data transformation methods, which may not optimally select images for improving model performance.
Innovation Solution
An information processing apparatus that executes active learning by dynamically selecting an image transformation method using a trained learning model. The apparatus includes an acquisition unit for acquiring a trained learning model, a first selection unit for selecting an image transformation method, and a second selection unit for selecting images to retrain the model using the chosen transformation method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed data transformation method is used in active learning, then the process is simple and consistent, but the model performance improvement is limited because the transformation method cannot adapt to different model states
Solution Approach 1:
The patent implements dynamic selection of data transformation methods based on the current model state. The system evaluates multiple transformation methods (geometric transformation, color transformation, noise addition) and selects the most appropriate one for each active learning iteration, allowing the transformation strategy to adapt to the model's evolving capabilities rather than using a fixed method throughout training.
Solution Approach 2:
The patent changes the parameter of transformation method selection based on model performance metrics. By monitoring model accuracy and uncertainty levels, the system adjusts which transformation methods are applied and how aggressively they are applied, enabling the system to adapt the transformation strategy to the current state of the learning model.
2Reliability
If a fixed data transformation method is used, then the system is easier to implement, but it cannot optimally select images for improving model performance
Solution Approach 1:
The patent incorporates feedback mechanisms where the system evaluates the effectiveness of different transformation methods by monitoring model performance changes. The feedback loop compares model accuracy before and after applying different transformations, and uses this information to select the most effective transformation method for the next active learning iteration, thereby improving image selection reliability.
Solution Approach 2:
The system performs self-evaluation of transformation methods by automatically testing different transformations on the data and selecting the most beneficial one based on observed model performance improvements. This self-service approach eliminates the need for manual intervention in transformation method selection while maintaining high reliability in image selection.
3Reliability
If large volumes of annotated data are used for training, then the machine learning model achieves better performance, but the work cost and time required increase enormously
Solution Approach 1:
The patent applies preliminary transformations to unannotated data before the active learning selection process. By pre-applying multiple transformation methods to the data pool, the system prepares transformed versions of images that can be efficiently evaluated and selected for annotation, reducing the overall time required for the active learning process while maintaining high model performance.
Solution Approach 2:
The system dynamically adjusts the parameters of data transformation based on the current model state and performance metrics. By changing transformation parameters adaptively, the system maximizes the information gain from each annotated example, thereby achieving high model performance with a smaller volume of annotated data compared to traditional methods.
Data Source
AI summary
An information processing apparatus that executes active learning by repeating image selection and retraining of a learning model with the selected images includes an acquisition unit configured to acquire a trained learning model, a first selection unit configured to select an image transformation method executed on an image by using the acquired learning model, and a second selection unit configured to select an image used to retrain the learning model by using the selected image transformation method and the acquired learning model.