Style-Transfer Neural Network for Image Recognition Training
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Solution Overview
Problem
Conventional image-recognition systems require large datasets of labeled training images to perform well, but they often struggle to generalize to a wide range of real-world conditions, necessitating a method to effectively varietize images for improved recognition performance.
Innovation Solution
The method involves clustering digital images based on texture or style features, selecting representative style images, training style-transfer neural networks to transfer these features to target images, and using the styled images to train image-recognition models, thereby reducing the need for extensive labeled datasets and improving model performance across various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets of labeled training images are used to train image-recognition systems, then recognition accuracy is improved, but the cost of data collection and labeling increases significantly
Solution Approach 1:
The patent uses style-transfer neural networks to generate synthetic training images by copying and transferring style features from source images to target images. This creates realistic-looking training data without requiring manual collection and labeling of numerous real images, thereby improving recognition accuracy while reducing the quantity of actual training data needed
Solution Approach 2:
The style-transfer neural network acts as an intermediary between source images and target images, transferring texture and style features to generate realistic synthetic training images. This mediator enables the system to create diverse training data without direct human intervention in image collection and labeling
2Adaptability or versatility
If diverse real-world conditions are represented in training data, then model generalization is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The patent segments image features into content features and style features, allowing independent manipulation of each. By separating style transfer from content, the system can efficiently generate diverse training images with different textures and conditions without complex data collection processes
Solution Approach 2:
The style-transfer neural network is pre-trained on diverse style images beforehand, enabling it to later generate varied training images on demand. This preliminary preparation allows the system to achieve good generalization without complex real-time data collection and processing
Data Source
AI summary
Systems and methods for image recognition are provided. A style-transfer neural network is trained for each real image to obtain a trained style-transfer neural network. The texture or style features of the real images are transferred, via the trained style-transfer neural network, to a target image to generate styled images which are used for training an image-recognition machine learning model (e.g., a neural network). In some cases, the real images are clustered and representative style images are selected from the clusters.