Efficient Learning of Convolution Weights as Gaussian Mixture Model Posteriors

Lifan Liang

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arXiv · 2401.17400

Efficient Learning of Convolution Weights as Gaussian Mixture Model Posteriors

Lifan Liang

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Abstract

In this paper, we showed that the feature map of a convolution layer is equivalent to the unnormalized log posterior of a special kind of Gaussian mixture for image modeling. Then we expanded the model to drive diverse features and proposed a corresponding EM algorithm to learn the model. Learning convolution weights using this approach is efficient, guaranteed to converge, and does not need supervised information. Code is available at: https://github.com/LifanLiang/CALM.

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Lifan Liang. 2024-07-01. Efficient Learning of Convolution Weights as Gaussian Mixture Model Posteriors. https://arxiv.org/abs/2401.17400

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