GAN Semantic Segmentation Using Embedding Loss for Thin Structures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing generative adversarial networks (GANs) for semantic segmentation, such as those used in the SegAN paper, are effective for large structures but struggle with accurately identifying thin structures like road divider markings due to the lack of explicit inter-pixel consistency in training loss functions.
Innovation Solution
A novel approach is introduced where a generative adversarial network is trained using a loss function that incorporates embedding differences between predicted and ground-truth segmentation maps, allowing the discriminator to provide more flexible feedback and improve the generator's output by considering the full image, segmentation map, and label data simultaneously, using an L2 distance metric for better performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a common multi-scale L1 loss function is used for training both generator and discriminator (as in SegAN), then training simplicity is improved, but segmentation accuracy for thin structures deteriorates
Solution Approach 1:
The loss function is segmented into multiple distinct components: a first loss function for the generator (combining pixel-wise loss and adversarial loss) and a second loss function for the discriminator (adversarial loss only). This segmentation allows each network to be optimized with appropriate loss terms, improving thin structure segmentation accuracy while maintaining training feasibility through modular design.
2Productivity
If per-pixel cross entropy loss is used for training, then training efficiency is improved, but inter-pixel consistency deteriorates
Solution Approach 1:
The loss function merges pixel-wise loss (L1 or cross entropy) with adversarial loss into a composite loss for the generator. This combination maintains the efficiency of pixel-wise training while adding the inter-pixel consistency enforcement through the adversarial component, where the discriminator learns to evaluate global segmentation quality.
3Manufacturing precision
If conditional random field or separate network post-processing is applied, then segmentation quality is improved, but system complexity increases
Solution Approach 1:
The system uses self-service by integrating the quality enforcement mechanism within the training process itself. The discriminator automatically learns to enforce inter-pixel consistency and guide the generator during training, eliminating the need for separate post-processing networks or conditional random fields. The generator learns to produce consistent segmentations directly through adversarial training.
Data Source
AI summary
A method is provided of training a generative adversarial network for performing semantic segmentation of images. The generative adversarial network includes a generator neural network and a discriminator neural network. The method includes providing an image as input to the generator neural network, receiving a predicted segmentation map for the image from the generator neural network, providing i) the image, ii) the predicted segmentation map, and iii) ground-truth label data corresponding to the image, as distinct training inputs to the discriminator neural network, determining a set of one or more outputs from the discriminator neural network in response to said training inputs, and training the generator neural network using a loss function that is a function of said set of outputs from the discriminator neural network.