Neural Classifier Training with Gradient Direction Analysis
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Solution Overview
Problem
Machine learning systems face a trade-off between learning from training data and overfitting, which degrades performance on new data.
Innovation Solution
The system employs cooperative training of multiple generators and a classifier, using a learning coach to control hyperparameters and impose restrictions, such as regularization and smooth augmentation, to mitigate overfitting while aggressively learning from data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems are trained aggressively to learn as much as possible from training data, then learning capacity is improved, but overfitting occurs which degrades performance on new data
Solution Approach 1:
The patent segments the training process into multiple independent generators, each trained on different subsets or aspects of the data. This segmentation allows the system to distribute the learning burden and reduce overfitting while maintaining aggressive learning capacity through the collective effort of multiple specialized generators.
Solution Approach 2:
The patent dynamically changes hyperparameters during training, including learning rates, regularization strengths, and data sampling strategies. This allows the system to adaptively balance between learning capacity and generalization, switching between aggressive learning modes and regularization modes as needed during the training process.
2Measurement precision
If multiple generators are trained cooperatively with different objectives, then learning effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple generators with different training objectives into a unified cooperative training framework. The generators work together on the same classification task, sharing data and learning outcomes, which improves learning effectiveness while the shared architecture reduces the overall complexity compared to fully independent systems.
Solution Approach 2:
The patent designs generators with multi-functionality, where each generator can serve multiple purposes: primary classification, auxiliary training for other generators, and adaptability to different data distributions. This universal design improves learning effectiveness across multiple objectives while reducing the need for separate specialized systems.
Data Source
AI summary
Computer-implemented systems and methods improve training of a neural network. Whether a target node is not decisive on a training data item is determined. Upon a determination that the target node is not decisive, a partial derivative of an objective for the target node is multiplied by a factor greater than 1.0 for the training data item. Determining whether the target node is not decisive can comprise determining whether a direction of the derivative is in a direction that would cause an update of learned parameters for the network to increase the difference between the activation value of the first target node for the training data item and a neutral activation value for the target node.