Panoptic Segmentation via Semi-Supervised GAN Domain Adaptation
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Solution Overview
Problem
Current deep learning models for panoptic segmentation face challenges in domain adaptation due to large domain gaps between synthetic and real-world data, requiring extensive manual labeling and suffering from performance issues when transferring between domains, with unsupervised methods like GANs not fully addressing compatibility with supervised learning.
Innovation Solution
A Semi-supervised Domain Adaptation approach using Generative Adversarial Networks (GANs) to train models separately for 'things' and 'stuff' in panoptic segmentation, iteratively leveraging unlabeled data to improve performance by bootstrapping from a limited labeled dataset, and fusing generated instances with real-world data to create updated datasets for incremental training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If unsupervised domain adaptation using GANs is used to bridge domain gaps, then manual labeling requirements are reduced, but task performance remains much worse than supervised learning
Solution Approach 1:
The patent segments the panoptic segmentation task into two separate GAN training processes: one for 'thing' classes (detectable objects) and another for 'stuff' classes (background regions). This segmentation allows each GAN to specialize in specific object types, improving overall task performance while still reducing manual labeling requirements compared to fully supervised methods.
Solution Approach 2:
The patent inverts the traditional domain adaptation approach by using generated images from GANs trained on source domain data to adapt the target domain model, rather than using manual labeled data from the target domain. The generated images serve as pseudo-labeled training data, enabling domain adaptation without extensive manual labeling while achieving performance comparable to supervised learning.
2Ease of manufacture
If synthetic data is used for training, then data collection is easier and cheaper, but large domain gaps exist between synthetic and real-world domains
Solution Approach 1:
The patent introduces GAN-generated images as an intermediary between synthetic training data and real-world deployment. The GANs are trained on synthetic data with ground truth labels, then generate realistic images that bridge the domain gap. These generated images serve as a mediator to adapt the model for real-world scenarios without requiring extensive manual labeling of real data.
Solution Approach 2:
The patent changes the parameters and characteristics of training data by using GANs to generate images with realistic statistical properties from synthetic data. By adjusting the GAN training process and using techniques like instance discrimination and category conditioning, the generated images capture real-world domain characteristics while retaining the benefits of synthetic data generation.
3Reliability
If extensive manual labeling is performed to achieve supervised learning performance, then task performance is high, but the amount of manual labeling required is huge
Solution Approach 1:
The patent performs preliminary action by training GANs on source domain data with available labels before deploying to the target domain. The GANs are pre-trained to generate realistic images that capture the essential characteristics of the target domain, so when deployed, the model can adapt quickly without requiring extensive manual labeling of target domain data.
Solution Approach 2:
The patent uses GANs to copy the essential characteristics and statistical properties of real-world data from synthetic data. The generated images serve as copies that preserve the important features needed for accurate segmentation while avoiding the need for manual labeling. This copying approach maintains high task performance while dramatically reducing manual labeling requirements.
Data Source
AI summary
Systems and methods for panoptic segmentation of an image of a scene, comprising: receiving a synthetic data set as simulation data set in a simulation domain, the simulation data set comprising a plurality of synthetic data objects; disentangling the synthetic data objects by class for a plurality of object classes; training each class of the plurality of classes separately by applying a Generative Adversarial Network (GAN) to each class from the data set in the simulation domain to create a generated instance for each class; combining the generated instances for each class with labels for the objects in each class to obtain a fake instance of an object; fusing the fake instances to create a fused image; and applying a GAN to the fused image and a corresponding real data set in a real-world domain to obtain an updated data set. The process can be repeated across multiple iterations.