InfoGAN inspired network trained on images from zap50k. This network is using continuous latent codes + noise as input to the generator. For the image similarity part, I extract the features of last layer before the classification layer of the discriminator for all images and calculate the l2 distances.
This project is using the following python packages:
tensorflow==0.11.0rc0
scipy==0.18.1
You will also need zap50k dataset:
wget http://vision.cs.utexas.edu/projects/finegrained/utzap50k/ut-zap50k-images.zip
To train the network on all the images:
python main.py --logdir=logs/exp1 --batch_size=128 --file_pattern="ut-zap50k-images/*/*/*/*.jpg"
To generate the intermediate features of all the images and calculate similarity for randomly picked images:
python main.py --similarity --batch_size=128 --logdir=logs/exp1 --file_pattern="ut-zap50k-images/*/*/*/*.jpg"
To get images from the generator:
python main.py --logdir=logs/exp1 --sampledir=samples
For each line, the first image is the seed and the rest are most similar images according to l2 distance of the intermediate features.
Samples from the generator after a coulpe of epochs.
