Efficient Learning of Convolution Weights as Gaussian Mixture Model Posteriors
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 30 Jan 2024.
Builds on
6 of 11 references.
References · 6
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
- 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,066
- 2013DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognitioncited by 424
- 2020SCAN: Learning to Classify Images without Labelscited by 352
- 2015Convolutional Clustering for Unsupervised Learningcited by 7
Built on by
Not cited yet.