Generative adversarial networks, or GANs, are frameworks for generative modeling. Generative modeling is an unsupervised learning approach that involves uncovering and studying patterns in data, then using it to generate new outputs. GANs are a way to train these generative models by framing the problem as a supervised learning problem, bifurcated into two sub-models, the generator model and the discriminator model. While the former generates new instances, the latter classifies the instances as either fake (generated) or real (from the domain). These models are solved in a zero-sum game, where both get better by competing against each other. Many sources are available to kick-start your knowledge of GANs and generative modeling. While books give you the most profound insight into the subject, finishing it is a big commitment. You can always start with some generative adversarial networks videos to get some idea.
We have enlisted some of the top generative adversarial network videos; have a look.
Top Generative Adversarial Networks Videos
Let us start with some introductory videos that will introduce Generative Adversarial Networks.
Introductory videos about GANs
- What are GANs? By IBM Technology
What are GANs is a small introductory video on generative networks. Developed by IBM Tech and presented by Martin Keen, this video briefs you about the bifurcation of GANs into the generator and discriminatory models. Keen begins by explaining the models, their functioning, and their outputs. He explains how the models compete against each other and how this competition benefits you. You will be able to define a GAN and understand how it works after watching the video.
- What are Generative Adversarial Networks?
This introductory YouTube video tutorial on GANs is a good place for beginners to learn what is meant by “generative,” “adversarial,” and “network.” it is posted by DigitalSreeni, a YouTube channel explaining several Python and AI-related topics. This video discusses deep learning architectures with two neural networks: a generator and a discriminator. It is a short video wherein Sreeni will only brief you about the concept and give you an overview of how to implement it by providing code snippets. Lastly, he ends the video by mentioning several applications of GANs, specifically SR-GAN, to generate high-resolution images.
- The Math Behind Generative Adversarial Networks Clearly Explained! By Normalized Nerd
The above two videos introduce you to the fundamentals of generative adversarial networks, and this video teaches you the core mathematics behind these models. You will need a background in statistics and advanced-level mathematics, as the video begins with defining the generator and discriminator using probability concepts.
The GAN video explains the computation behind a generative model in generating results and a discriminator model in predicting them. It talks less about theoretical concepts and is conversationally practical by showcasing all the formulas used. For more information, you can refer to the original paper from which the content has been taken.
Some more exciting videos about GANs
Now that you know the basics, you can check out some more detailed videos on GANs.
- Generative Adversarial Networks and TF-GAN by TensorFlow
This generative adversarial network video is a part of the Machine Learning Tech Talks hosted by TensorFlow. Research engineer Joel Shor talks about GANs as the recent development of machine learning technologies and an open-source library, TF-GAN, for training and evaluating GANs.
Shor begins by describing GANs and their applications, then delves into the metrics. You need to have a statistical and mathematical background to understand the metrics. Lastly, he discusses how to develop a self-attention GAN and get started working with these networks.
- Ian Goodfellow: Generative Adversarial Networks, NIPS 2016 Tutorial
This video session, delivered by Dr. Ian Goodfellow, is an insightful discussion for those without experience with generative adversarial networks. Dr. Goodfellow is the man behind this class of machine learning frameworks and aims to promote a greater audience to understand and utilize GANs to improve on other core algorithms. He describes GANs as “universal approximators” of probability distributions and requires a few approximations like Markov’s chain, variational bounds, or Monte Carlo for generating possible learning results.
While watching the video, you will learn about the entire learning process of the adversarial game between the generator and the discriminator. The video also covers the Jensen-Shannon divergence as an extended GAN framework, applications of GANs, research frontiers, and several improved model architectures.
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- Conditional GANs and their applications by DigitalSreeni
All the generative adversarial networks videos mentioned in this list talk about generative adversarial networks or GANs, except this one focus on conditional GANs or cGANs. Initially, the video talks about standard GANs and their usefulness in generating random images from the domain.
You will learn that the standard GANs can be conditioned using specific image modalities and the methods that generate them. This conditioning is done by feeding the class labels in both adversarial models. The video also discusses applications like image-to-image translation, CycleGAN, super-resolution, and text-to-image synthesis.
- Generative Adversarial Networks by Coursera
Coursera offers several courses on generative adversarial networks or GANs. These courses contain video lectures about fundamental concepts, applications, and challenges in learning and deploying GANs. The course “Build Basic Generative Adversarial Networks” is a part of the GAN specialization offered by Coursera that introduces you to the concept’s intuition and helps you build conditional GANs, training models using PyTorch and also covers the social implications of using such networks.
This video specialization offers a straightforward route for learners of all skill levels who want to explore GANs or use GANs in their projects, even if they have no prior knowledge of advanced mathematics or machine learning research.
- GANs and Autoencoders by Argonne Leadership Computing Facility
This generative adversarial network video features a session of ALCF AI for Science Training and introduces you to applying GANs and autoencoders in scientific research. Presented by Corey Adams, an assistant computer scientist at the Argonne Leadership Computing Facility, it is a detailed video discussing an ongoing ALCF research project, the study problem, the theoretical solution, and the codes.
Technically, you will learn about how these frameworks work and the institutional differences in their learning process. It is one of the most appropriate generative adversarial networks videos if you are interested in learning about autoencoders and their application in solving a semisupervised learning problem and GANs in solving an unsupervised learning problem.
- Improved Consistency Regularization for GANs
This is one of those generative adversarial networks videos that uncovers a new technique to enhance consistency regularization, a model training technique invariant to data augmentations in semi-supervised learning. It discusses using the same regularization methods in unsupervised learning methods like SimCLR and FixMatch in the GAN. You will learn that doing so significantly improves the FID scores (Frechet Inception DIstance, a quality evaluation metric for generated images) for generating images.
- Business Applications of GANs and Reinforcement Learning by Dataiku
If you want to learn about real-life applications of GANs, it is a great video posted by Dataiku. In this video, Alex Combessie, a data scientist at Dataiku, talks about the business applications of GAN AI technologies. GANs have succeeded in synthetic image generation, but can they be applied to forecast option prices?
Combessie shares the story of two data scientists who deployed a GAN for option pricing. Specifically, he discusses real-time option pricing by explaining the gaussian assumption in the Black-Scholes formula. Learning about pricing will be wise if you are interested in trading options contracts. Hence, this video is a great place to start if you have a background in AI, adversarial networks, or related technologies and wish to learn about their application in options trading.
