Neural Style Transfer for Targeted Data Augmentation
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Solution Overview
Problem
Training deep neural networks requires large amounts of labeled data, and manual labeling of custom datasets is costly, tedious, and error-prone, especially when the dataset style differs from the general dataset used for pre-training.
Innovation Solution
The method employs neural style transfer to transform existing training data into images with a target style, allowing existing labels to be reused for custom datasets, using a style transformer network that maintains photorealistic quality and style consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual labeling is performed on custom datasets to match the target style, then the training data quality and style consistency are improved, but the time consumption and cost increase significantly
Solution Approach 1:
The patent uses a pre-trained style transfer model to copy the target style from reference images and apply it to source images, generating synthetic training data that mimics the appearance and characteristics of manually labeled data without requiring actual manual labeling. This copying approach preserves training data quality while eliminating the time-consuming manual labeling process.
Solution Approach 2:
The style transfer model is pre-trained on a large dataset of paired images before being used for generating custom training data. This preliminary training action enables the model to learn style transformations in advance, so that when actual training data generation is needed, the process is already optimized and ready to produce high-quality results quickly.
2Reliability
If a large amount of custom labeled training data is collected to match the target style, then the model generalizability is improved, but the cost and complexity of data collection and labeling increase
Solution Approach 1:
The system uses automated style transfer technology to generate training data independently without requiring human intervention for labeling. The style transfer model automatically analyzes source images, extracts target style characteristics, and generates synthesized training images with consistent styling, enabling the system to serve itself in data generation without external labeling resources.
Solution Approach 2:
The patent transforms the style parameters of images by adjusting color distributions, texture characteristics, and other visual parameters through the style transfer model. By changing these parameters algorithmically rather than through manual collection, the system generates diverse training data with controlled variability that improves model generalizability while reducing data collection complexity.
3Quantity of substance
If general data augmentation transformations are applied to existing training data, then the training data quantity is increased, but the data does not match the target style required for the specific application
Solution Approach 1:
The style transfer model serves multiple functions simultaneously: it performs style transfer, data augmentation, and style adaptation in a single unified system. The same model can adapt to different target styles by changing the reference images, making it universally applicable to various application scenarios without requiring separate systems for each style requirement.
Solution Approach 2:
The patent combines elements from source images and target style reference images to create composite training images that contain both the content structure of the source and the stylistic characteristics of the target. This composite approach produces training data that simultaneously increases quantity and achieves style adaptability by merging beneficial features from both inputs.
Data Source
AI summary
A method for training a deep neural network (DNN) to perform a specified task with respect to images captured by a target camera, including: using an image captured by the target camera as a style target image, training a style transformer network to perform a style transformation that transforms any photorealistic input image into a transformed image that has contents of the input image, maintains photorealistic quality of the input image, and has a style that matches a style of the style target image; using the trained style transformer network to transform training image of an original training dataset into transformed training images; labeling the transformed training images with the training labels of the corresponding training image of the original training dataset, to form an augmented training dataset; and using the augmented training dataset to train the DNN to perform the specified task.