Relative Margin Contrastive Learning for Vanishing Gradient Control
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Solution Overview
Problem
Conventional contrastive pre-training methods are limited by vanishing gradients and noisy data, leading to suboptimal performance, especially in low-shot and zero-shot applications with high-separation pairs and noisy training examples.
Innovation Solution
Applying a relative margin to higher-separation training pairs using a linear function to adjust the contribution of pairs to optimization, enhancing the contrastive loss function for improved encoder neural network training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional contrastive pre-training methods are used, then training is performed on available data, but gradient strength vanishes and noisy data degrades performance
Solution Approach 1:
The patent modifies the contrastive loss function by introducing a dynamic margin parameter that changes based on the separation distance between embedding pairs. This parameter change transforms the fixed gradient behavior into a dynamic one where gradient strength is preserved for high-separation pairs while maintaining effectiveness for low-separation pairs, directly resolving the vanishing gradient problem
2Productivity
If conventional contrastive pre-training is applied, then all training pairs are treated equally, but noisy examples harm performance
Solution Approach 1:
The patent applies different treatment to different regions of the embedding space by introducing a distance-dependent margin. Pairs with large separation distances receive larger margins that amplify their gradient contribution, while pairs with small separation distances receive smaller margins. This local differentiation allows the model to selectively emphasize reliable high-separation pairs while downweighting noisy low-separation pairs, improving overall training reliability
3Quantity of substance
If standard contrastive loss is used, then optimization is performed uniformly across all pairs, but high-separation pairs contribute insufficiently to gradient updates
Solution Approach 1:
The patent introduces a dynamic margin parameter that adapts based on the separation distance between embedding pairs. This dynamic adjustment transforms the static contrastive loss into a flexible one where the margin automatically increases for high-separation pairs, ensuring they contribute adequate gradient strength. The dynamic nature of the margin allows the system to maintain effectiveness across diverse pair separations without requiring manual intervention
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks through contrastive learning. In particular, the contrastive learning is modified to use a relative margin to adjust a training pair's contribution to optimization.