cifar10_dense
    Train a neural network with convolutional layers on Cifar10
    Accuracy: 46.23%, 49.84%, 48.33% (1e-2)
    Accuracy: 52.77% after 10 iterations (1e-3)

mxnet_cifar10_cnn
    MXNet implementation of above, runs each epoch twice as fast (from 2 minutes to 1 minute)

cvae_cnn_cifar10
    Train a conditional VAE with convolutional layers on Cifar10
    The goal is to use the autoencoder aspect of VAE to improve training. Basically, the interpretation I have is that the autoencoder forces the model to learn a more global descriptor of the images, and the variational aspect regularizes the model and maybe makes the model more continuous.
    Accuracy: 60% by 7th iteration

mxnet_cvae_cnn_cifar10
    MXNet version of above, one epoch takes 6 minutes versus 4 minutes in Pytorch (so it's slower)

gvae_cat_mnist
    Try to classify MNIST using a VAE which contains Gaussians and Categorical encodings
    Wasn't able to get better than 55% accuracy
    There are many different hyperparameters to tweak
    I would guess if a good set of hyperparameters are chosen, the results will be better than gvae_mnist

gvae_mnist
    Try to classify MNIST using a VAE which only has a Gaussian
    Was able to get 80% accuracy after 10 epochs (clearly not as good as using a regular model which approaches 95% after 10 epochs)

infovae/ vae_cat_dense / vae_cat_dense_2
    Implementation of VAEs with Gumbel Softmax distribution for categorical features
    By having the reconstruction term and normal distribution term weigh more heavily than the categorical term, the results causes the model to learn the different numbers per category. This has to do with not forcing the distribution of categorical to be uniform.
    The mathematical backing of this is that it's being trained to be close to a uniform distribution. If we add a Dirichlet prior with a small alpha, the probability of the uniform distribution will be low causing the term to be scaled down (or the other terms scaled up).

mnist_cnn
    Classify MNIST dataset using a Convolutional Network

mnist_dense
    Classify MNIST dataset using only Dense Layers

utils
    Contains OpenCV utilities (read and writing images)

vae_cnn_cifar10
    Use VAE on Cifar10

vae_cnn_mnist
    Use VAE for classification of MNIST (similar to cvae_cnn_cifar10)

vae_dense
    Use VAE on MNIST

vae_dense_mnist
    Add Variational Autoencoder to see if training a NN on MNIST improves.

vae_vgg
    Doesn't work
    Tried to use VGG16 model for features and then use those features as a part of the loss function similar to style transfer. Basically, it would be a way to improve on the L2 loss which causes blurry images to a loss function that is dependent on the features of the image

vgg
    Object that loads in the VGG16 model