From fb70ab05768fa4a54358dc1f304b68bc7aff6dae Mon Sep 17 00:00:00 2001
From: Jules Laplace <julescarbon@gmail.com>
Date: Sun, 8 Dec 2019 21:43:30 +0100
Subject: inversion json files

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+Exploiting GAN Internal Capacity for High-Quality Reconstruction of Natural Images
+==================================================================================
+
+Code for reproducing experiments in ["Exploiting GAN Internal Capacity for High-Quality Reconstruction of Natural Images"](https://arxiv.org/abs/1911.05630)
+
+This directory contains associated source code to invert BigGAN generator for
+128x128 resolution. Requires Tensorflow.
+
+## Generation of Random Samples:
+Generate 1000 random samples of BigGAN generator:
+```console
+  $> python random_sample.py random_sample.json
+```
+
+## Inversion of the Generator:
+The optimization is split into two steps according to the paper:
+First step, invesion to the latent space:
+```console
+  $> python inversion.py params_latent.json
+```
+
+Second step, inversion to the dense layer:
+```console
+  $> python inversion.py params_dense.json
+```
+
+## Interpolation:
+Generate interpolations between the inverted images and generated images:
+```console
+  $> python interpolation.py params_dense.json
+```
+
+## Segmentation:
+Segment inverted images by clustering the attention map:
+```console
+  $> python segmentation.py params_dense.json
+```
+
+Note: to replicate the experiments on real images from ImageNet, first
+a hdf5 file must be created with random images from the dataset, similar to the
+procedure in "random_sample.py". Then, the two step of optimization must be
+executed (modify the "dataset:" parameter in params_latent.json to consider
+custom datasets).
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