Overview Of Variational Autoencoder Based Generative Models
This is an article about using variational autoencoders for the generation of new data. It contains the code for generating the plots and training the mentioned models on celeb_a.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Brain Inspired Replay | 169 | 2 years ago | 1 | mit | Python | |||||
| A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code). | ||||||||||
| Variationalautoencoders | 17 | 6 years ago | Jupyter Notebook | |||||||
| Comparison of Variational Autoencoders with Bayesian Neural Networks. Accuracy, Latent space, Reconstruction and White Noise filtering. | ||||||||||
| Overview Of Variational Autoencoder Based Generative Models | 11 | 3 years ago | TeX | |||||||
| This is an article about using variational autoencoders for the generation of new data. It contains the code for generating the plots and training the mentioned models on celeb_a. | ||||||||||
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