Deep Image Model Translation for Synthetic Data Augmentation
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Solution Overview
Problem
Machine learning systems face challenges in recognizing objects within images due to sparse and poor-quality real-world training data, leading to inadequate performance in object recognition tasks, especially when deep learning algorithms require large amounts of data that are costly and time-consuming to acquire.
Innovation Solution
The implementation of Deep Image Model Translation Generation (DMTG) methods, which use image translation and generation techniques to supplement training data sets with synthetic images, leveraging convolutional autoencoders and generative adversarial networks to convert sparse and low-quality images into high-quality, diverse training data, thereby enriching the training dataset without the need for extensive real-world data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If synthetic computer-generated examples are used to train machine learning systems, then data acquisition cost and time are reduced, but the training quality and recognition performance deteriorate due to the significant difference between synthetic and real-world data
Solution Approach 1:
The patent introduces a domain adaptation layer as an intermediary component that bridges the gap between synthetic source domain data and real-world target domain data. This layer learns the domain shift characteristics and transforms synthetic examples to better match real-world distributions, thereby maintaining the efficiency benefits of synthetic data while improving training quality and recognition performance
Solution Approach 2:
The patent employs parameter optimization techniques to adjust the characteristics of synthetic data generation and domain adaptation processes. By dynamically modifying parameters such as transformation intensities, mixing ratios, and adaptation strengths, the system optimizes the balance between using synthetic data efficiency and achieving real-world performance accuracy
2Reliability
If large amounts of real-world training data are collected to train deep learning algorithms, then object recognition performance is improved, but data acquisition and preprocessing costs increase significantly
Solution Approach 1:
The patent creates augmented versions of limited real-world training data through various transformation operations including geometric transformations, color space conversions, and noise additions. This copying and transformation process generates diverse training examples from a small seed set, reducing the need to acquire large amounts of original real-world data while maintaining recognition performance
Solution Approach 2:
The patent develops a unified training framework that can effectively utilize both synthetic and real-world data together. This multi-functional approach allows the system to leverage the efficiency benefits of synthetic data generation while incorporating the authenticity of real-world examples, achieving high performance with reduced overall data acquisition requirements
3Quantity of substance
If synthetic training images are used to supplement the training dataset, then data sparsity is reduced, but the quality and realism of training data deteriorate
Solution Approach 1:
The patent merges synthetic training images with real-world training images in a combined training dataset. By integrating both types of data and applying domain adaptation techniques to harmonize their distributions, the system achieves sufficient data volume to train deep learning models effectively while maintaining the quality and realism needed for accurate object recognition
Data Source
AI summary
Embodiments of the present invention relate to systems and methods for improving the training of machine learning systems to recognize certain objects within a given image by supplementing an existing sparse set of real-world training images with a comparatively dense set of realistic training images. Embodiments may create such a dense set of realistic training images by training a machine learning translator with a convolutional autoencoder to translate a dense set of synthetic images of an object into more realistic training images. Embodiments may also create a dense set of realistic training images by training a generative adversarial network (“GAN”) to create realistic training images from a combination of the existing sparse set of real-world training images and either Gaussian noise, translated images, or synthetic images. The created dense set of realistic training images may then be used to more effectively train a machine learning object recognizer to recognize a target object in a newly presented digital image.