GAN Target Domain Generation via Multi-Loss Feature Alignment
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Solution Overview
Problem
Existing methods for generating target data using Generative Adversarial Networks (GAN) often result in reduced accuracy due to differences in feature distributions between source and target data, as the GAN transforms data to resemble real data without reflecting the features of the source model.
Innovation Solution
An electronic apparatus and method that reconstructs source data, trains it to generate target data, and applies loss values such as class loss, distance loss, cluster loss, CAM loss, and feature loss to ensure the target data aligns with the source model's features, thereby improving accuracy by maintaining the distribution of the source data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GAN transforms source data to resemble real data in the target domain, then the realism of generated data is improved, but the accuracy in the target domain deteriorates due to feature distribution mismatch
Solution Approach 1:
The patent applies multiple loss functions (class loss, distance loss, cluster loss, CAM loss, feature loss) to control and adjust the feature distribution parameters of generated data, ensuring alignment between source and target domains while maintaining realism
Solution Approach 2:
The patent combines multiple loss functions into a composite loss framework, where each loss function addresses different aspects of feature alignment (class structure, distance relationships, cluster distribution, CAM features, and general feature consistency) to achieve comprehensive domain adaptation
2Quantity of substance
If GAN generates fake data similar to real data, then the quantity of target data is improved, but the reliability of the generated data deteriorates due to loss of source model features
Solution Approach 1:
The patent implements feedback mechanisms through multiple loss functions that continuously monitor and adjust the generated data to maintain consistency with source model features, including class loss for category consistency, distance loss for relationship preservation, and feature loss for overall feature alignment
Solution Approach 2:
The patent applies preliminary constraints through loss functions during the data generation process to prevent feature distribution drift before it occurs, ensuring that generated data maintains source model characteristics from the outset rather than correcting them afterward
Data Source
AI summary
An electronic apparatus generating a generative adversarial network (GAN)-based target domain and a generating method thereof is provided. The generating method includes reconstructing source data included in a source domain, generating target data by training the reconstructed source data based on the source data, and generating a target domain including the generated target data, and the training comprises identifying at least one loss value between a class loss value by class loss and a distance loss value by distance matrix loss and applying at least one loss value between the identified class loss value and the distance loss value to the reconstructed source data.