Synthetic Image Generation for Low-Shot Learning via 3D Mesh
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Solution Overview
Problem
Current machine learning models face challenges in low-shot learning scenarios where they need to classify novel classes with scarce annotated data, often relying on unsupervised or weakly supervised approaches that compromise performance due to data scarcity and skewed availability of annotated data.
Innovation Solution
The system generates synthetic novel class images based on a 3D mesh and texture maps learned from base class images, using a self-paced learning strategy and generative adversarial networks to enhance data diversity and robustness, thereby improving the performance of low-shot learning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If unsupervised or weakly supervised approaches are used for low-shot learning, then data annotation requirements are reduced, but classification performance deteriorates due to data scarcity
Solution Approach 1:
The patent uses generative adversarial networks to create synthetic copies of training images for novel classes. The GAN generates realistic synthetic images that replicate the visual characteristics of target objects, enabling the model to learn from these artificial copies when real annotated data is scarce, thus maintaining high classification performance without requiring extensive manual annotation
Solution Approach 2:
The system performs preliminary unsupervised pre-training on base classes before fine-tuning on novel classes. This two-stage approach allows the model to first learn general features from abundant base class data, then adapt to novel classes with minimal annotated samples, effectively preparing the model in advance for low-shot scenarios
2Measurement precision
If more annotated data is collected for novel classes, then classification accuracy improves, but data collection time and cost increase
Solution Approach 1:
Instead of collecting additional real annotated images of novel classes, the system generates synthetic copies using GANs trained on base class data. This copying approach provides sufficient training examples for novel classes without requiring time-consuming field collection and annotation of rare or difficult-to-obtain images
Solution Approach 2:
The system uses a universal base class that can generate synthetic data for multiple novel classes. The learned representations from base classes are transferable across different novel class targets, allowing a single pre-training phase to benefit multiple downstream classification tasks with minimal class-specific data collection
3Reliability
If synthetic images are generated to expand data diversity, then classification performance improves, but the risk of generating unrealistic images increases
Solution Approach 1:
The patent introduces a discriminator network as an intermediary that acts as a quality gatekeeper. The discriminator is trained to distinguish real from synthetic images, providing feedback to the generator to improve realism. This intermediary mechanism ensures that only high-quality, realistic synthetic images are used for training, maintaining both performance and visual fidelity
Solution Approach 2:
The generative adversarial framework implements continuous feedback between the generator and discriminator. The discriminator's classification feedback guides the generator to produce more realistic images, while the generator's improved outputs provide better training signals to the discriminator, creating a self-correcting system that maintains image quality
Data Source
AI summary
In one aspect, there is provided a system including at least one data processor and at least one memory. The at least one memory may store instructions that cause operations when executed by the at least one data processor. The operations may include retrieving a set of authentic base class images from a database. The operations may further include generating, based on the set of authentic base class images, a three dimensional mesh of the base class. The operations may further include retrieving a set of authentic novel class images. The operations may further include generating, at a first neural network and based on the three dimensional mesh and the set of authentic novel class images, a set of synthetic novel class images. The operations may further include training a second neural network based on the set of synthetic novel class images.