A suite of image generation models that produce images from simple random processes
Learning to See by Looking at Noise
In this work, we investigate a suite of image generation models that produce images from simple random processes. These are then used as training data for a visual representation learner with a contrastive loss. We study two types of noise processes, statistical image models and deep generative models under different random initializations.
Requirements
This version of code has been tested with Python 3.7.7 and pytorch 1.6.0. Other versions of pytorch are likely to work out of the box. The contrastive training requires two GPU’s with at least 12GB of memory for the small scale experiments, while the large scale experiments require the same computation resources as the facebookresearch implementation of MoCo.
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