Adaptive Regularization Strength Control for Deep Learning
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Solution Overview
Problem
Conventional regularization techniques in deep learning are uniform across all training data, leading to overfitting for simple data and reduced efficiency for difficult data, as they do not adaptively control the strength of regularization based on the training data.
Innovation Solution
A learning device and method that calculates weights using a rapidly increasing weight function and applies a monotonically increasing rescale function to the regularization term, optimizing the inference model with a total loss that adjusts the regularization strength based on the training data, thereby adapting the regularization strength for each mini-batch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If uniform regularization is applied to all training data, then overfitting is suppressed for simple data, but learning efficiency is reduced for difficult data
Solution Approach 1:
The patent applies different regularization strengths to different training samples based on their individual characteristics. Specifically, samples with high model confidence receive stronger regularization, while samples with low confidence receive weaker regularization. This local differentiation resolves the contradiction by tailoring the regularization effect to each sample's needs rather than applying a uniform approach.
Solution Approach 2:
The regularization strength is dynamically adjusted during training based on the model's predicted confidence for each sample. The confidence score, derived from the inference model's output, serves as a dynamic parameter that modulates the regularization term. This dynamic adjustment allows the system to adaptively balance between preventing overfitting and maintaining learning efficiency.
2Reliability
If strong regularization is applied to difficult training data, then overfitting is prevented, but learning efficiency is reduced
Solution Approach 1:
The patent selectively applies strong regularization only to samples where the model exhibits high confidence, which are more prone to overfitting. For difficult samples with low confidence, the regularization strength is reduced, allowing the model to learn from these challenging cases without excessive penalty. This localized application optimizes the balance between preventing overfitting and preserving training efficiency.
Solution Approach 2:
The regularization parameter is changed based on the confidence score of each sample. By modulating the regularization strength as a variable parameter rather than a fixed value, the system can adaptively respond to different sample characteristics, reducing unnecessary regularization for difficult samples and thereby reducing training time loss.
3Productivity
If weak regularization is applied to simple training data, then learning efficiency is improved, but overfitting occurs
Solution Approach 1:
The patent identifies simple samples through high confidence scores and applies stronger regularization specifically to these cases. This local quality approach ensures that overfitting-prone samples receive appropriate regularization strength, while not unnecessarily penalizing difficult samples that benefit from weaker regularization.
Solution Approach 2:
The system uses the model's own confidence predictions as feedback to determine the appropriate regularization strength for each sample. This feedback mechanism allows the system to automatically identify which samples are prone to overfitting and apply corresponding regularization, creating a self-regulating process that prevents overfitting while maintaining efficiency.
Data Source
AI summary
In a learning device, an inference means performs an inference with respect to training data using an inference model, and outputs a class score. A weight calculation means calculates each weight using a weight function which rapidly increases faster than a linear function for the class score that is over-estimated or under-estimated, based on output the class score. A weight sum calculation means calculates a total of weights over a mini-batch included in a predetermined number of training data. A regularization term calculation means calculates a regularization term by applying a rescale function which is a monotonically increasing function gradually increasing more than a linear function, to the regularization term. An optimization means optimizes the inference model using a total loss including the regularization term.