Efficient Learning of Convolution Weights as Gaussian Mixture Model Posteriors

• URL: http://arxiv.org/abs/2401.17400v2
• Date: Mon, 1 Jul 2024 20:57:47 GMT
• Title: Efficient Learning of Convolution Weights as Gaussian Mixture Model Posteriors
• Authors: Lifan Liang,
• Abstract summary: We show 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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