Image super-resolution through deep learning. This project uses deep learning to upscale 16x16 images by a 4x factor. The resulting 64x64 images display sharp features that are plausible based on the dataset that was used to train the neural net.
Here's an random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16x16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth.
As you can see, the network is able to produce a very plausible reconstruction of the original face. As the dataset is mainly composed of well-illuminated faces looking straight ahead, the reconstruction is poorer when the face is at an angle, poorly illuminated, or partially occluded by eyeglasses or hands.
This particular example was produced after training the network for 3 hours on a GTX 1080 GPU, equivalent to 130,000 batches or about 10 epochs.
In essence the architecture is a generative adversarial networks (GANs) where the input to the generator network is the 16x16 image rather than a multinomial gaussian distribution.
In addition to that the loss function of the generator has a term that measures the L1 difference between the 16x16 input and downscaled version of the image produced by the generator.
The adversarial term of the loss function ensures the generator produces plausible faces, while the L1 term ensures that those faces resemble the low-res input data. We have found that this L1 term greatly accelerates the convergence of the network during the first batches and also appears to prevent the generator from getting stuck in a poor local solution.
Finally, the generator network relies on ResNet modules as we've found them to train substantially faster than more old-fashioned architectures. The adversarial network is much simpler as the use of ResNet modules did not provide an advantage during our experimentation.
We provided options (or flags) in 'srez_main.py' to change the components of the model. Three different training objectives were implemented: the vanilla GAN (Goodfellow et al., 2014), WGAN (Arjovsky & Bottou, 2017) and the improved WGAN (Gulrajani et al., 2017). Other changable options include optimizers (Adam/RMSprop), architectures (DCGAN/ ResNet), normalization (batch/ layer), and input data (low dimensional noise/ downscaled images). This repo was used for a systematic investigation on the application of the newly proposed Wasserstein GAN objective (and its improved version). We also checked the claims of WGAN, namely stable and converging training on a variety of architectures, and the effectiveness of the Wasserstein distance as an indicator of training progress. For more details, please read our paper at https://arxiv.org/abs/1705.02438.
You will need Python 3 with Tensorflow, numpy, scipy and moviepy. See requirements.txt for details.
After you have the required software above you will also need the Large-scale CelebFaces Attributes (CelebA) Dataset. The model expects the Align&Cropped Images version. Extract all images to a subfolder named dataset. I.e. srez/dataset/lotsoffiles.jpg. You are also welcome to use 'download_data.py'.
Training with default settings: python3 srez_main.py --run train. The script will periodically output an example batch in PNG format onto the srez/train folder, and checkpoint data will be stored in the srez/checkpoint folder. You can also use 'run_script.sh' to set particular parameters and shut down computing instances on cloud after training finishes.
After the network has trained you can also produce an animation showing the evolution of the output by running python3 srez_main.py --run demo. This script generates a demo using checkpoints.
The current repo is maintained by: Zhimin Chen (https://mandyzchen.github.io/)
This github repo was forked from David Garcia: LinkedIn profile of David Garcia.
Goodfellow, Ian J. (2014) On distinguishability criteria for estimating generative models. arXiv preprint arXiv:1412.6515.
Arjovsky, M., & Bottou, L. (2017). Towards principled methods for training generative adversarial networks. In NIPS 2016 Workshop on Adversarial Training.
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved Training of Wasserstein GANs. arXiv preprint arXiv:1704.00028.