hwalsuklee/tensorflow-generative-model-collections

Features

• TensorFlow Model Definitions - Constructs generative model architectures using the TensorFlow framework to define computational graphs and layers.
• Generative Adversarial Networks - Provides a comprehensive set of Generative Adversarial Network architectures to produce synthetic data from noise vectors.
• Wasserstein GAN Setups - Implements Wasserstein GAN architectures using distance-based loss to improve training stability.
• Conditional Generative Modeling - Implements conditional generative modeling by merging external information into the generation process.
• Latent Space Sampling - Generates synthetic data priors by sampling from Gaussian or Uniform distributions within the latent space.
• EBGAN Implementations - Implements an Embedding GAN (EBGAN) architecture with an auto-encoder style discriminator.
• WGAN-GP Implementations - Implements the WGAN-GP model to produce realistic images with improved training stability.
• Synthetic Image Generators - Utilizes neural networks and image processing pipelines to create realistic artificial imagery.
• TensorFlow Model Development - Builds a collection of deep learning architectures for synthetic data generation specifically using the TensorFlow framework.
• Variational Autoencoder Pipelines - Implements a Variational Autoencoder pipeline using Gaussian latent spaces and Bernoulli distributions for data synthesis.
• Variational Autoencoders - Implements variational autoencoder architectures using Gaussian encoders and Bernoulli decoders to synthesize new data.
• Conditioning Vector Injection - Provides mechanisms to guide image generation by injecting conditioning vectors and labels into the model feature maps.
• Fully Connected Layers - Implements fully connected layers for linear transformations of feature vectors.
• Latent Space Disentanglement - Implements InfoGAN to learn disentangled representations by combining noise vectors with latent codes.
• Latent Space Manipulations - Provides mechanisms for latent space manipulation, including the use of conditioning vectors and disentangled representations.
• Image Discriminators - Implements discriminator networks used to evaluate the authenticity of generated images in adversarial training.
• Least Squares GANs - Implements a Generative Adversarial Network using a least squares loss function to improve training stability and image quality.
• Convolution Layers - Implements 2D and transposed convolutional layers for processing spatial image data.
• Normalization Layers - Provides normalization layers to scale activations and stabilize training convergence.
• Neural Network Layers - Provides reusable components for 2D convolutions, linear transformations, and activation normalization.
• Training Stability Techniques - Employs normalization layers and Wasserstein GP loss functions to improve training convergence and stability.
• Gradient Penalties - Implements gradient penalty mechanisms to stabilize discriminator training and prevent mode collapse.
• Transposed Convolutions - Uses transposed convolutions to upsample low-dimensional latent vectors into full-resolution images.
• Tensor Processing Pipelines - Includes a pipeline for normalizing, cropping, and grid-merging image tensors to prepare data for neural network training.
• Generative Model Implementations - TensorFlow implementations of diverse generative models.
• Generative Models - Generative model implementations for TensorFlow.

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