Cross-Domain Translation Network for Generative Neural Network Adaptation
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Solution Overview
Problem
Conventional generative adversarial networks (GANs) face inefficiencies and inaccuracies when adapting to target domains with a small number of sample images, requiring extensive data and computation resources, and struggle to maintain correspondence between source and target domains in image generation.
Innovation Solution
A neural network adaptation system uses a cross-domain translation network to fine-tune a generative neural network pretrained on a large source domain, aligning results with the target domain by mapping images back to the source domain for comparison, adjusting parameters based on visual features to improve correspondence and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional GANs are trained on a small target domain, then the network can be adapted to the target domain, but the training efficiency is low and the generation accuracy is poor
Solution Approach 1:
The system pre-trains the GAN on a large source domain before adapting it to the target domain. This preliminary training on abundant source data provides a strong foundation that accelerates subsequent adaptation to the small target domain, resolving the contradiction between training efficiency and generation accuracy.
Solution Approach 2:
The system introduces a cross-domain translation network as an intermediary that maps between source and target domains. This intermediary enables knowledge transfer from the source domain to the target domain, allowing the GAN to achieve high generation accuracy on the small target domain without requiring extensive target domain training data.
2Manufacturing precision
If extensive data and computation resources are used for training, then the generation accuracy improves, but the computation resources and time required increase
Solution Approach 1:
The GAN is pre-trained on a large source domain in advance, performing the computationally intensive training task beforehand. This allows the actual target domain adaptation to be completed quickly with minimal additional training time, while still achieving high generation accuracy through the accumulated knowledge from source domain training.
Solution Approach 2:
The system extracts the essential generative capabilities and knowledge from the source domain training and applies them to the target domain. By separating the heavy computational burden of initial training from the final target domain adaptation, the system achieves high accuracy without requiring extensive training time for the specific target application.
3Adaptability or versatility
If the network is adapted to the target domain, then the results correspond to the target domain, but the correspondence between source and target domain features deteriorates
Solution Approach 1:
The cross-domain translation network serves as an intermediary that maintains feature correspondence between source and target domains. It translates features across domains while preserving the essential semantic relationships, allowing the GAN to adapt to the target domain while maintaining accurate feature correspondence through the translation bridge.
Solution Approach 2:
The system uses the cross-domain translation network to provide feedback that ensures feature correspondence is maintained during target domain adaptation. The translation network monitors and adjusts the feature representations to preserve correspondence relationships, enabling the GAN to adapt to the target domain without losing feature alignment.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for adapting generative neural networks to target domains utilizing an image translation neural network. In particular, in one or more embodiments, the disclosed systems utilize an image translation neural network to translate target results to a source domain for input in target neural network adaptation. For instance, in some embodiments, the disclosed systems compare a translated target result with a source result from a pretrained source generative neural network to adjust parameters of a target generative neural network to produce results corresponding in features to source results and corresponding in style to the target domain.