Regularized Partial Gradients for Label Noise Robustness
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Solution Overview
Problem
Machine learning models face challenges during training due to noisy datasets and exploding gradients, which inhibit successful learning and degrade model performance, with conventional gradient clipping techniques failing to address label noise effectively.
Innovation Solution
The system generates regularized partial gradients of losses computed using an objective function composed of a base loss and a link function, specifically regularizing the partial gradient of the base loss while preserving the intactness of the link function's gradient, to mitigate the impact of label noise and exploding gradients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If conventional gradient clipping techniques are used to address exploding gradients, then gradient stability is improved, but robustness against label noise deteriorates
Solution Approach 1:
The gradient clipping operation is segmented to apply only to the base loss gradient component, while the link function gradient component remains unclipped. This selective segmentation allows the base loss gradient to be stabilized against exploding gradients while preserving the link function's ability to handle label noise effectively
Solution Approach 2:
Different clipping strategies are applied to different components of the gradient. The base loss gradient receives gradient clipping to ensure stability, while the link function gradient maintains its original form to preserve noise robustness. This local differentiation of gradient treatment resolves the contradiction between stability and robustness
2Productivity
If the norm of the base loss gradient exceeds a threshold and clipping is applied, then gradient magnitude is controlled, but information from the link function gradient is preserved
Solution Approach 1:
The total gradient is segmented into two distinct components: base loss gradient and link function gradient. The clipping operation is applied only to the base loss gradient component, while the link function gradient component is left intact and unmodified, thus preventing information loss while controlling gradient magnitude
Solution Approach 2:
The base loss gradient acts as an intermediary that undergoes clipping to control magnitude, while the link function gradient is preserved as a separate entity. This intermediary approach allows gradient magnitude control without sacrificing the informational content of the link function gradient
Data Source
AI summary
A method for training a machine learning model, e.g., a neural network, using a regularization scheme is disclosed. The method includes generating regularized partial gradients of losses computed using an objective function for training the machine learning model.