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[WIP] Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery [Research] #8750

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Vishal-V
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@Vishal-V Vishal-V commented Jun 29, 2020

Description

📝 Implement Hierarchical Disentanglement for Fine-Grained Object Generation - FineGAN.

Type of change

  • A new research paper code implementation

Key Features

  • TensorFlow 2.2.0
  • Inference example (Colab Demo)
  • Transfer learning example (Inception-ResNet Backbone)
  • Eager mode training with tf.GradientTape [If Required]
  • [Default] Graph mode training with model.train_on_batch
  • Functional model with tf.keras.layers.Layer
  • Subclassing model with tf.keras.Model
  • Input pipeline using tf.data and tfds
  • Tensorflow Serving
  • Vectorized transformations
  • GPU accelerated
  • Fully integrated with absl-py from abseil.io
  • Clean implementation
  • Following the best practices
  • Apache 2.0 License

Tests

📝 [WIP] Code Reviews and Tests Pending.

  • [WIP] Provide instructions so we can reproduce.
  • [WIP] Please also list any relevant details for your test configuration.

Test Configuration:

  • Config .yml files and Config class included.

Checklist

@Vishal-V Vishal-V changed the title [WIP] Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery - Implementation [WIP] Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery [Research] Jun 29, 2020
@jaeyounkim
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As we discussed, please submit a pull request for the community directory.

@jaeyounkim jaeyounkim closed this Aug 14, 2020
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3 participants