Semantic Segmentation Loss for More Realistic GAN Image Generation
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Solution Overview
Problem
Existing image generation algorithms, such as GAN-based super-resolution algorithms, produce images that are plausible but not realistic, lacking the desired level of realism, and existing techniques to improve image quality come at substantial hardware and latency costs.
Innovation Solution
Training a semantic segmentation network to generate class-wise probability maps that are sensitive to picture quality, incorporating these maps into the loss function of an image generation network to enhance the realism of generated images without increasing hardware costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GAN-based super-resolution algorithms are used to generate high-resolution images, then image realism is improved, but the generated images appear artificial and not natural-looking
Solution Approach 1:
The patent segments the image generation process by introducing a semantic segmentation network that divides the image into distinct semantic regions (sky, water, land, etc.). This segmentation allows the model to apply different generation strategies to different regions, improving natural appearance by ensuring semantically consistent textures and structures in each region while maintaining overall realism through the GAN framework
Solution Approach 2:
The patent applies local quality by using semantic segmentation to identify different regions of the image and applying region-specific loss functions and generation parameters. Each semantic region receives tailored processing with appropriate texture synthesis and detail generation, allowing the model to optimize for natural appearance in specific areas (like water surfaces or foliage) while maintaining global coherence
2Reliability
If semantic segmentation networks are integrated into image generation pipelines to improve picture quality, then image realism is enhanced, but hardware deployment costs and latency increase
Solution Approach 1:
The patent applies preliminary action by pre-training the semantic segmentation network separately from the image generation network. The segmentation network is trained offline to accurately identify semantic regions, and its weights are frozen during the main image generation training. This preliminary preparation allows the generation network to leverage semantic information without requiring real-time segmentation computation, reducing hardware complexity and deployment costs while maintaining picture quality improvements
Data Source
AI summary
A method includes training a semantic segmentation network to generate semantic segmentation maps having class-wise probability values. The method also includes generating a semantic segmentation map using the trained semantic segmentation network. The method further includes utilizing the semantic segmentation map during training of an image generation network as part of a loss function that includes multiple losses. The semantic segmentation network may be trained to be sensitive to picture quality of an output image generated by the image generation network during the training of the image generation network such that increased degradation of the picture quality of the output image results in decreased prediction confidence by the semantic segmentation network. The semantic segmentation network may be trained to vary the class-wise probability values based on the picture quality.