Clustering-Based Regularization for Interpretable Deep Neural Networks
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Solution Overview
Problem
Deep learning classification models, particularly deep neural networks (DNNs), face challenges with overfitting due to their complex decision boundaries and lack of sparsity in activations, making them difficult to interpret and embed in mobile devices, and requiring extensive annotations for training interpretable models.
Innovation Solution
A clustering-based regularization process is introduced during DNN training, which adds a regularization activity penalty to the loss function to induce sparse activations, allowing neurons to converge to a prior probability distribution that encourages clustering of filters or neurons associated with specific object parts or concepts without the need for explicit annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are trained with many parameters to achieve high accuracy, then classification performance is improved, but the model becomes harder to interpret and requires more memory
Solution Approach 1:
The patent segments the high-dimensional activation space by introducing clustering-based regularization that groups neurons into distinct clusters. Each cluster represents a specific concept or object part, making the complex model interpretable by dividing its internal representations into meaningful segments without reducing the total number of parameters.
Solution Approach 2:
The patent changes the parameter distribution within the network by applying regularization that encourages sparse activations and clustered neuron behavior. This transforms the parameter usage from dense and scattered to sparse and clustered, improving interpretability while maintaining the same number of parameters for high accuracy.
2Measurement precision
If deep neural networks use many parameters to form complex decision boundaries, then classification performance is improved, but overfitting risk increases
Solution Approach 1:
The patent introduces dynamic regularization during training that adaptively encourages sparse and clustered activations. This dynamic constraint changes the optimization landscape to favor solutions with better generalization properties, reducing overfitting while maintaining high accuracy on training data.
Solution Approach 2:
The patent modifies the effective parameter usage through regularization that induces sparsity and clustering. This changes how parameters are utilized during inference, creating more robust decision boundaries that generalize better to unseen data without sacrificing training accuracy.
3Adaptability or versatility
If filters are designed to fire in response to multiple object parts, then representation coverage is improved, but interpretability and sparsity are reduced
Solution Approach 1:
The patent segments the filter response space by applying clustering regularization that organizes neurons into distinct clusters, each specializing in specific object parts or concepts. This segmentation maintains comprehensive coverage through multiple clusters while ensuring each individual filter remains interpretable by focusing on specific segments.
Solution Approach 2:
The patent achieves universality at the cluster level rather than the individual filter level. Each cluster can handle multiple related object parts, providing versatile representation coverage, while individual filters within clusters maintain specificity for interpretability. This multi-level organization balances coverage and sparsity.
Data Source
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AI summary
A computer-implemented method of training a deep neural network to classify data comprises: for a batch of N training data Xi, where i = 1 to N and ci is the class of training data Xi, carrying out a clustering-based regularization process at at least one layer l of the DNN having neurons j, in which process a regularization activity penalty is added to a loss function for the batch of training data which is to be optimized during training, whereby the regularization activity penalty comprises components associated with respective neurons in the layer which are dependent on the respective classes of the training data.