mbsariyildiz / autoencoder-pytorch

Autoencoder implementation in PyTorch.

Currently trainer supports only the celebA dataset.

Requirements

• PyTorch v0.4.0
• torchvision
• tqdm
• tensorboard_logger

Comments and Implementation Details:

• I performed all experiments over the aligned images in celebA.
• There are three parameters used to split the celebA: red_ratio can be used to reduce training set size in order to tune hyperparameters quickly, test_split determines the ratio between training and test splits, validation_split determines how much of the training set will be used as the validation set.
• To run on CPU or GPU supporting the cuda, set device argument to 'cpu' or 'cuda' respectively.
• Don't forget to arange folders for exp_dir and data_dir. data_dir is assumed to point /some_path/celebA/img_align_celeba.

Sample Reconstructions:

The image below depicts some reconstructions from the test set. While I was experimenting with several random reconstructions, I realized that the model only cares about face and hair. The network learns to delete, for instance, hands or necklaces.

Interpolation between two images:

Interpolations in the latent codes of two images. Top and bottom rows show the weighted averages of the original images and the reconstructions of interpolated latent codes, respectively.

Interpolation over the dimensions:

In order to see how each dimension effects the reconstructed image, I made some sequential tiny changes to each dimension separetely and reconstructed the latent code. In the image below, middle column is the original image, and each row represents changes over a dimension (top:0th dimension, bottom:127th dimension). As you go left and right in each step 0.2 is subtracted and added to the corresponding dimension, respectively.

When you look at each row carefully, you can see that small changes over each dimension changes the appearance of the lady.