Unleashing the potential of GANs: a look at popular Open-Source GAN Models
Generative adversarial networks, or GANs, are a type of machine learning algorithm that can be used to generate synthetic datasets. These algorithms consist of two parts: a generator network that produces synthetic data, and a discriminator network that attempts to distinguish the synthetic from real data. We’ll discuss some of the available open-source GANs below, including TensorFlow GAN, Pix2pix, and CycleGAN, which are available on popular code hosting platforms such as GitHub. We’ll also discuss some applications of GANs in healthcare and climate change, such as generating novel molecular structures and generating realistic images of climate change effects.
TensorFlow GAN (TF-GAN)
TF-GAN is an open-source software library with easy-to-use functions for setting up and training GANs but also allows for manual control of each step for advanced users. TF-GAN is versatile and can be used with a variety of GAN setups, such as training a generator to sample from a learned distribution or incorporating additional information through conditioning. Several published papers have employed TF-GAN in their research, including Self-Attention Generative Adversarial Networks, Large Scale GAN Training for High Fidelity Natural Image Synthesis, and GANSynth: Adversarial Neural Audio Synthesis.
Pix2pix is a method for image-to-image translation, which involves training a model on a dataset of aligned image pairs, where the input images and output images are related in some way. For example, input images could be black-and-white sketches of objects, while output images could be color photographs of the same objects. During training, the model learns to generate output images that are similar to the corresponding target images in the training set.
Once the model is trained, it can be used to generate output images similar to a given input image. This allows the model to perform a variety of image translation tasks, such as synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images.
In many cases, paired training data may not be available (i.e., one cannot use architectures like Pix2pix explained above), thus requiring an approach that can learn to translate an image from a source domain to a target domain without paired examples. CycleGAN aims to learn a mapping such that the distribution of images generated using the source domain is indistinguishable from the distribution of real images in the target domain. This approach has been tested on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, and photo enhancement. Corresponding results can be found here.
Finding new chemical compounds with certain desired properties is crucial for many applications, such as in de novo drug design. The space of synthesizable molecules is vast, making it challenging to search for compounds with specific properties, as explained in this article. MolGAN is a model that uses GANs and reinforcement learning to generate small molecular graphs. In experiments on the QM9 chemical database, it is able to generate almost 100% valid compounds. It outperforms other methods that use string-based (SMILES) representations of molecules and a likelihood-based method that directly generates graphs, although it is susceptible to mode collapse.
The authors of this article have developed a system that allows users to easily visualize the potential consequences of extreme climate events, such as flooding, in familiar places. By simply entering an address, users can view a first-person image from Google Street View transformed as if it had been affected by extreme flooding. This innovative system uses a GAN model called ClimateGAN as its engine, which incorporates a Masker model to accurately predict which parts of an image would be underwater in the event of a flood, and a Painter model to generate photo-realistic water textures based on the input image and the Masker’s prediction. By projecting the potential consequences of climate change in a tangible and relatable way, this system can help to make the abstract impacts of climate change more concrete and encourage action to prevent its potentially devastating effects.
Jukebox is a powerful music production system that utilizes GANs to generate songs from a variety of genres, including rock, hip-hop, and jazz. Its capabilities include the ability to analyze and replicate the melodies, rhythms, and timbres of various instruments and voices, as well as the ability to generate novel versions of existing songs. Jukebox also allows users to customize the generation process by using a conditional prior, which allows for the specification of lyrics or control of the composition through the use of midi data, allowing for the generation of highly customized and unique musical compositions. More detailed information can be found in this paper.
GANs are thus a powerful tool in machine learning that can generate synthetic datasets for a variety of purposes. Open-source Generative AI models such as those listed above offer a range of capabilities and have the potential to significantly impact various fields, important societal, scientific, and creative fields. GANs, therefore, have the potential to revolutionize the way we approach these fields by providing access to large amounts of synthetic data for research and development.