Abstract
TorchGAN is a PyTorch based framework for writing succinct and comprehensible code for training and evaluation of Generative Adversarial Networks. The framework's modular design allows effortless customization of the model architecture, loss functions, training paradigms, and evaluation metrics. The key features of TorchGAN are its extensibility, built-in support for a large number of popular models, losses and evaluation metrics, and zero overhead compared to vanilla PyTorch. By using the framework to implement several popular GAN models, we demonstrate its extensibility and ease of use. We also benchmark the training time of our framework for said models against the corresponding baseline PyTorch implementations and observe that TorchGAN's features bear almost zero overhead.
Highlights
The key features of TorchGAN are its extensibility, built-in support for a large number of popular models, losses and evaluation metrics, and zero overhead compared to vanilla PyTorch
Generative Adversarial Networks (GANs) (Goodfellow et al, 2014) are a class of deep generative models that formulate the model estimation problem as an adversarial game between two neural networks, a Generator representing an implicit generative distribution, and a Discriminator that differentiates between samples from said implicit distribution and the true data distribution
We provide an overview of the features that are provided off the shelf by TorchGAN and compare them with the ones provided by other frameworks
Summary
Generative Adversarial Networks (GANs) (Goodfellow et al, 2014) are a class of deep generative models that formulate the model estimation problem as an adversarial game between two neural networks, a Generator representing an implicit generative distribution, and a Discriminator that differentiates between samples from said implicit distribution and the true data distribution. GANs generally share a standard design paradigm, with the building blocks comprising one or more generator and discriminator models, and the associated loss functions for training them. TorchGAN makes use of this design similarity by exposing a simple API for customizing these blocks. The interaction between these components at training time is facilitated by a highly robust trainer which automatically adapts to user-defined GAN models and losses. TorchGAN provides an extensive and continually expanding collection of popular GAN models, losses, evaluation metrics, and stability-enhancing features, which can either be used off the shelf or extended or combined to design more sophisticated models effortlessly. With the above design principles in mind, we aim to improve upon existing GAN training frameworks such as TFGAN (Shor, 2017), HyperGAN (Community, 2016), and IBM GAN-Toolkit (Raunak Sinha, 2018) on the aspects of extensibility, the richness of the feature set and documentation
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