GAUSSIAN MIXTURE VAE: LESSONS IN VARIATIONAL INFERENCE, GENERATIVE MODELS, AND DEEP NETS
Not too long ago, I came across this paper on unsupervised clustering with Gaussian Mixture VAEs. I was quite surprised, especially since I had worked on a very similar (maybe the same?) concept a few
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- 1. DEEP UNSUPERVISED CLUSTERING WITH GAUSSIAN MIXTURE VARIATIONAL AUTOENCODERS（ICLR2017）
- 2. 讀論文——Variational inference with Gaussian mixture model and householder flow
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相關文章
- 1. DEEP UNSUPERVISED CLUSTERING WITH GAUSSIAN MIXTURE VARIATIONAL AUTOENCODERS（ICLR2017）
- 2. 讀論文——Variational inference with Gaussian mixture model and householder flow
- 3. [Scikit-learn] 2.1 Clustering - Variational Bayesian Gaussian Mixture
- 4. Generative Models - 一文理解Variational Autoencoder
- 5. Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding(DGG)
- 6. Gaussian mixture model
- 7. [Scikit-learn] 2.1 Clustering - Gaussian mixture models & EM
- 8. Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
- 9. [ECCV 2020] DeepGMR: Learning Latent Gaussian Mixture Models for Registration
- 10. Generative Adversarial Nets[CycleGAN]