Add README.md with info on released BigBiGAN TF Hub modules.

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bigbigan/README.md

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+# Pretrained BigBiGAN models
+
+We have released pretrained BigBiGAN models used for unsupervised image
+generation and representation learning, as described in our July 2019
+tech report,
+["Large Scale Adversarial Representation Learning"](https://arxiv.org/abs/1907.02544) [1].
+
+The pretrained models are available for use via [TF Hub](https://tfhub.dev/s?publisher=deepmind&q=bigbigan).
+The release includes two BigBiGAN models with different encoder architectures:
+
+* Small encoder (ResNet-50): [https://tfhub.dev/deepmind/bigbigan-resnet50/1](https://tfhub.dev/deepmind/bigbigan-resnet50/1)
+* Large encoder (RevNet-50 x4): [https://tfhub.dev/deepmind/bigbigan-revnet50x4/1](https://tfhub.dev/deepmind/bigbigan-revnet50x4/1)
+
+See the TF Hub pages linked above for documentation and example usage of each module.
+
+## Demo (Colab)
+
+A Google Colab-based demo with example usage of the model functionality and sample visualization is available [here](//colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/bigbigan_with_tf_hub.ipynb).
+
+## Example use
+The snippet below demonstrates the use of the released TF Hub modules for
+image generation/reconstruction and encoder feature computation.
+(The [Colab demo](//colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/bigbigan_with_tf_hub.ipynb)
+includes more extensive documentation and visualizations.)
+
+```python
+# Load BigBiGAN module.
+module = hub.Module('https://tfhub.dev/deepmind/bigbigan-resnet50/1')  # small encoder
+# module = hub.Module('https://tfhub.dev/deepmind/bigbigan-revnet50x4/1')  # large encoder
+
+# Sample a batch of 8 random latent vectors (z) from the Gaussian prior. Then
+# call the generator on the latent samples to generate a batch of images with
+# shape [8, 128, 128, 3] and range [-1, 1].
+z = tf.random.normal([8, 120])  # latent samples
+gen_samples = module(z, signature='generate')
+
+# Given a batch of 256x256 RGB images in range [-1, 1], call the encoder to
+# compute predicted latents z and other features (e.g. for use in downstream
+# recognition tasks).
+images = tf.placeholder(tf.float32, shape=[None, 256, 256, 3])
+features = module(images, signature='encode', as_dict=True)
+
+# Get the predicted latent sample `z_sample` from the dict of features.
+# Other available features include `avepool_feat` and `bn_crelu_feat`, used in
+# the representation learning results.
+z_sample = features['z_sample']  # shape [?, 120]
+
+# Compute reconstructions of the input `images` by passing the encoder's output
+# `z_sample` back through the generator. Note that raw generator outputs are
+# half the resolution of encoder inputs (128x128). To get upsampled generator
+# outputs matching the encoder input resolution (256x256), instead use:
+#     recons = module(z_sample, signature='generate', as_dict=True)['upsampled']
+recons = module(z_sample, signature='generate')  # shape [?, 128, 128, 3]
+```
+
+## References
+
+[1] Jeff Donahue and Karen Simonyan.
+[Large Scale Adversarial Representation Learning](https://arxiv.org/abs/1907.02544).
+*arxiv:1907.02544*, 2019.