Feature-Space Guided Training for Synthetic-to-Real Classification
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Solution Overview
Problem
Machine learning systems trained with synthetic data often perform poorly compared to those trained with real-world data, leading to a domain gap in performance when applied to real-world scenarios.
Innovation Solution
A computer-implemented method for training a machine learning system using synthetic data, which incorporates a guidance term in the objective function to measure similarity with feature representations from a pretrained system trained on real-world data, thereby adjusting the training process to improve generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning system is trained with synthetic defective images generated by deep generative models, then the training process becomes faster and more feasible, but the performance and generalization ability on real-world data deteriorates
Solution Approach 1:
The patent introduces an intermediary alignment mechanism that bridges the synthetic and real data domains. By computing feature representations from both synthetic and real images through the trained model and applying gradient-based optimization to minimize the distributional distance between these features, the system creates a mediating alignment layer that allows synthetic data to effectively guide training while maintaining compatibility with real-world distributions, thus resolving the performance gap
Solution Approach 2:
The patent dynamically adjusts training parameters by computing adaptive gradients that modulate the influence of synthetic data based on the current state of model convergence. The alignment loss function modifies gradient magnitudes and directions during training, allowing the system to automatically balance between learning from synthetic data and maintaining fidelity to real-world patterns, thereby improving generalization without sacrificing training efficiency
2Stability of the object's composition
If feature representations from synthetic samples are pushed apart to prevent collapsing, then model robustness improves, but feature representation quality and similarity to real-world features deteriorates
Solution Approach 1:
The patent employs a counterbalancing strategy by introducing an alignment loss term that acts as a counterweight to the feature pushing-apart mechanism. While one component of the objective function encourages divergence of synthetic feature representations to prevent mode collapse, the alignment component simultaneously pulls these features toward the real data distribution manifold, creating a balanced force system that maintains both stability and fidelity
Solution Approach 2:
The patent implements a dynamic training regime where the relative influence of feature divergence and alignment forces evolves during training. The system adaptively modulates the strength of alignment constraints based on training progress and feature distribution characteristics, allowing the model to first establish stable synthetic feature representations and then progressively align them with real-world patterns, achieving both stability and precision
Data Source
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AI summary
A computer implemented method of training a first machine learning system for object classification by optimizing an objective function, using a set of training data, wherein said first machine learning system is trained based on said objective function, comprising a guidance term that measures a similarity between a span of feature representations in a feature space of said first machine learning system and a corresponding span of feature representations in a feature space of a second machine learning system for object classification.