Real-world data can be multimodal distributed, e.g., data describing the opinion divergence in a community, the interspike interval distribution of neurons, and the oscillators natural frequencies. Generating multimodal distributed real-world data has become a challenge to existing generative adversarial networks (GANs). For example, neural stochastic differential equations (Neural SDEs), treated as infinite-dimensional GANs, have demonstrated successful performance mainly in generating unimodal time series data. In this paper, we propose a novel time series generator, named directed chain GANs (DC-GANs), which inserts a time series dataset (called a neighborhood process of the directed chain or input) into the drift and diffusion coefficients of the directed chain SDEs with distributional constraints. DC-GANs can generate new time series of the same distribution as the neighborhood process, and the neighborhood process will provide the key step in learning and generating multimodal distributed time series. The proposed DC-GANs are examined on four datasets, including two stochastic models from social sciences and computational neuroscience, and two real-world datasets on stock prices and energy consumption. To our best knowledge, DC-GANs are the first work that can generate multimodal time series data and consistently outperforms state-of-the-art benchmarks with respect to measures of distribution, data similarity, and predictive ability.

Art is an artistic method of using digital technologies as a part of the generative or creative process. With the advent of digital currency and NFTs (Non-Fungible Token), the demand for digital art is growing aggressively. In this manuscript, we advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training. We compare various architectures and designs of DC-GANs to arrive at a recommendable design choice for a stable and realistic generation. The main focus of the work is to generate realistic images that do not exist in reality but are synthesised from random noise by the proposed model. We provide visual results of generated animal face images (some pieces of evidence showing a blend of species) along with recommendations for training, architecture and design choices. We also show how training image preprocessing plays a massive role in GAN training.

I recently came across the Stanford MedAI Youtube Channel where each week a speaker is invited to give a talk on a topic related to Medical AI, and I would highly recommend you check them out. In this week's talk by Jason Jeong on the applications of Generative Adversarial Networks (GANs) in Medical Imaging, he mentions the paper titled "How to Fool Radiologists with Generative Adversarial Networks?". I was immediately drawn to the clever title and went to check the paper out myself. In this paper published in 2018 by Chuquicusma et al., unsupervised learning with Deep Convolutional Generative Adversarial Networks (DC-GANs) was used to generate realistic-looking images of lung nodules. Two radiologists were then asked to undertake a visual turing test where they were asked to determine whether a lung nodule was fake or real.

Generative adversarial neural networks (GANs) [1] [2] [3] [4] are deep learning (DL) models whereby a dataset is used by an agent, called the generator, to sample white noise from a latent space and simulate a data distribution to create new (fake) data that resemble the original data it has been trained on. Another agent, called the discriminator, has to correctly discern between the original data (provided by the external environment for training) and the fake data (produced by the generator). The generator prevails over the discriminator if the latter does not succeed in distinguishing anymore the original from the fake. The discriminator prevails over the generator if the fake data created by the generator is categorized as fake, and the original data is still categorized as original. An illustration that describes a GANs model is shown in Figure 1.

Using Deep Learning DC-GAN to add featured effect on anything. After my final project submission and earning a Certificate of Accomplishment in the course I just want to share with you what I did. May be this could help someone well understand and use DCGAN. For this project I chose to create an application to wear eyeglasses or hats to people without glasses or hats, using DCGAN (Deep Convolutional Generative Adversarial Networks) and hat or/and eyeglass vectors through the VGG model network we used during the course. DC-GAN uses AutoEncoder (AE) and GAN (Generative Adversarial Networks) to generate a featured output according to the input you fit in it.

Most of us in data science have seen a lot of AI-generated people in recent times, whether it be in papers, blogs, or videos. We've reached a stage where it's becoming increasingly difficult to distinguish between actual human faces and faces generated by artificial intelligence. However, with the current available machine learning toolkits, creating these images yourself is not as difficult as you might think. In my view, GANs will change the way we generate video games and special effects. Using this approach, we could create realistic textures or characters on demand.