Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search

This repository contains the code for the following paper arXiv:

Masanori Suganuma, Mete Ozay, and Takayuki Okatani, "Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search," arXiv:1803.00370, (2018)

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Requirement

• Ubuntu 14.04 LTS
• CUDA version 8.0
• Python version 3.6.2
• Pytortch version 0.2.0_4

Usage

Run the architecture search (denoising)

python exp_main.py -i

Run the architecture search (inpainting)

python exp_main.py -i -mask center

When you specify the -i option, an initial individual consists of a single convolution layer and a single deconvolution layer. To choose inpainting tasks, please specify the -mask option (center, pixel, half).

When you use the multiple GPUs, please specify the -g option (default:1):

python exp_main.py -g 2

You can set the number of offsprings with the -l option (default:2):

python exp_main.py -l 4

After the execution, the files, network_info.pickle and log_cgp.txt will be generated. The file network_info.pickle contains the information for Cartegian genetic programming (CGP) and log_cgp.txt contains the log of the optimization and discovered CAE architecture's genotype lists.

Some parameters (e.g., # rows and columns of CGP, and # epochs) can easily change by modifying the arguments in the script exp_main.py.

Re-training (fine-tuning)

To re-train the discovered architecture:

python exp_main.py -m retrain

Re-evolution

To re-start the evolution:

python exp_main.py -m reevolution

Dataset

To create the training, validation, and test sets of the CelebA dataset:

python create_data.py

The cars dataset is available here.

The SVHN dataset is available here.

The BSDS500 dataset is available here.