GAN Image and Label Map Generation with Masked Global Pooling
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Solution Overview
Problem
The challenge of efficiently generating labeled data for computer vision tasks like semantic segmentation and object detection is significant due to the time-consuming nature of expert human labeling, necessitating a method for automated label generation and accurate classification of images.
Innovation Solution
A generative adversarial network (GAN) that generates images and corresponding label maps, utilizing a generator to produce images and label maps, and a discriminator to classify the authenticity of the generated data, employing masking and global pooling operations to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert human labeling is used for semantic segmentation or object detection, then labeling accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent uses a generative adversarial network to create synthetic labeled images that copy the structure and characteristics of real labeled images. The generator produces realistic images with corresponding accurate label maps, eliminating the need for manual human labeling while preserving labeling accuracy through the adversarial training process between generator and discriminator
Solution Approach 2:
The system performs automated self-labeling through the GAN framework. The generator automatically generates both images and their corresponding label maps without human intervention, and the discriminator automatically validates the authenticity of generated images and labels, creating a self-sufficient labeling system
2Reliability
If a large and diverse dataset is used for training machine learning systems, then generalization capability is improved, but data gathering and labeling difficulty increase
Solution Approach 1:
The GAN system generates synthetic training data that copies the statistical properties and visual characteristics of real data distributions. By training on generated images with ground-truth label maps, the system achieves good generalization capability without the complexity of gathering and labeling large diverse real-world datasets
Solution Approach 2:
The system performs preliminary data preparation by generating a diverse dataset with accurate labels before training the machine learning model. The GAN is trained beforehand to learn the data distribution and generate realistic samples, which are then used as training data, eliminating the need for complex real-world data collection and preprocessing
3Productivity
If conventional GANs are used for image generation, then image generation capability is achieved, but accurate label map generation and classification accuracy are insufficient
Solution Approach 1:
The patent merges image generation and label map generation into a single unified generator network. The generator simultaneously produces both the image and its corresponding label map from the same latent representation, ensuring consistent and accurate labeling. The discriminator also merges image authentication and label validation, checking both the image authenticity and label accuracy together through integrated feature map masking and pooling operations
Solution Approach 2:
The adversarial training process provides continuous feedback to improve both image quality and label accuracy. The discriminator's feedback through the loss function guides the generator to produce more realistic images with more accurate labels, while the masking and global pooling operations provide specific feedback on label map quality, creating a closed-loop optimization system
Data Source
AI summary
A generative adversarial network. The generative adversarial network includes: a generator configured for generating an image and a corresponding label map; a discriminator configured for determining a classification of a provided image and a provided label map, wherein the classification characterizes whether the provided image and the provided label map have been generated by the generator or not and determining the classification comprises the steps of: determining a first feature map of the provided image; masking the first feature map according to the provided label map thereby determining a masked feature map; globally pooling the masked feature map thereby determining a feature representation of the provided image masked by the provided label map; determining a classification of the image based on the feature representation.