Hitchhiker’s Guide to Generative Adversarial Networks (GANs)
Unsupervised learning has always been a tough problem to crack for researchers and practitioners. The last few years, however, have seen huge strides being made here with the widespread development of generative models, or specifically Generative Adversarial Networks (GANs).
GANs are eating the (deep learning) world, would not be an exaggeration when referring to the past year. Research groups and companies have come out with multiple variations, optimization techniques and network architectures to boost the capabilities of GANs.
The aim of this talk is to provide a comprehensive introduction to GANs, understand their working principles, provide an overview of recent developments and also peek into the future that lies ahead.
every project described in the presentation comes with a hyperlinked reference on the bottom-right of the slide - encouraged to click through to know more about each project
Motivation + Overview of Generative Networks [5 mins]
- Reasoning for interest in generative networks
- Overview of options for such networks
- Issues and motivations for developing GANs
GANs - Fundamentals [12 mins]
Introduce the framework under which GANs can be studied. The following ideas will be discussed to enable a clear understanding of the fundaments
- GANs as games played betweene adversarial networks
- Generator v. Discriminator networks and “goals” for each network
- Loss function options and training process for GANs
- DCGAN architecture
GANs - Applications and Recent Developments [10 mins]
An overview of developments in the past year. Touching upon each of the following, covering how they were developed, salient contributions and applications that can be realized.
- SRGANs for single image super-resolution
- Interactive GANs for image generation
- pix2pix, image to image transalation
- Conditional GANs for text to image synthesis
- Image inpainting using DCGANs
- Using multiple GANs for cross-domain application and “style transfer”
Issues & Improvements [8 mins]
Overview of the issues affecting GANs and the steps that are being taken to combat them.
- Recent developments that are helping solve some of the known stability problems
- Tricks to optimize development and training of GANs
- Looking at impact of GANs going forward and effect on other aspects of generative modelling
Basic understanding of the current capabilities of deep learning
I am a member of the data science team at Semantics3 - building data-powered software for ecommerce-focused companies. Over the years, I have had the chance to dabble in various fields covering data processing, pipeline setup, database management and data science. When not picking locks, or scuba diving, I usually blog about my technical adventures at our team’s engineering blog.
- Tutorial: Generative Adversarial Networks, NIPS 2016, Ian Goodfellow - https://arxiv.org/abs/1701.00160
- Major deep learning advancements in 2016 (with a focus on GANs) - https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/
- My other technical articles - https://engineering.semantics3.com/authors/ramananbalakrishnan/
- LinkedIn - https://www.linkedin.com/in/ramananbalakrishnan