s-omranpour/X-VAE-keras
simple implementations of different kinds of VAE in tf.keras
This is a resource for machine learning researchers and practitioners who want to explore and implement various types of Variational Autoencoders (VAEs). It provides ready-to-use TensorFlow Keras implementations for different VAE architectures, allowing you to input your data and experiment with generating new samples or learning compressed representations. This is for those studying or applying generative models in deep learning.
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Use this if you are a machine learning researcher or student looking for straightforward Keras implementations of diverse VAE models to understand their mechanisms or apply them to datasets like MNIST.
Not ideal if you are looking for a high-level API to immediately deploy complex generative models in production without needing to understand the underlying VAE variants.
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12
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3
Language
Jupyter Notebook
License
GPL-3.0
Last pushed
Dec 04, 2019
Commits (30d)
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