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-A collection of Variational AutoEncoders (VAEs) implemented in PyTorch with focus on reproducibility. The aim of this project is to provide
+A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. The aim of this project is to provide
 a quick and simple working example for many of the cool VAE models out there. All the models are trained on the [CelebA dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)
 for consistency and comparison. The architecture of all the models are kept as similar as possible with the same layers, except for cases where the original paper necessitates 
 a radically different architecture (Ex. VQ VAE uses Residual layers and no Batch-Norm, unlike other models).