##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
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- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
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Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more
PP
Pushkar Pushp
@ppushp7
GAN-inspired Innovations in Computer Vision
Submitted Apr 30, 2019
Section:
Tutorials
Technical level:
Intermediate
Session type:
Lecture
"The most interesting idea in the last 10 years in ML.” - Yann LeCun, Facebook AI research director.
In this talk, we will focus on Generative Adversarial Networks, one of the most interesting concepts in deep learning. A GAN is a generative model, which captures the patterns in the data so that it can generate new data points from the estimated data distribution. In the recent years, there has been tremendous research in the field of GANs, some of which include text-to-image synthesis, photo realistic image generation from doodles and a lot more.
We will cover the working of GANs with implementation and some of these interesting applications in this talk.
Keywords : StackGAN , DCGAN, Generators, Autoencoders, VAE
Outline
Generative vs Discriminative Models
Why GANs?
Introduction to GAN
How do GANs work?
Generators and Discriminators
Cost function and optimization
GANs vs Autoencoders and VAE
DCGAN
Recent applications/case studies of GANs
Text-to-image synthesis
StackGANs
Pose Guided Person Image Generation
SRGAN
Nvidia’s GauGAN (Doodles into photo realistic images)
Deep Fakes
We will also cover an implementation of DCGAN using Jupyter notebook and keras for better understanding of the implementation and the concept.
Requirements
Basic understanding of deep learning and how neural networks are trained. Beginner level knowledge about Python and Keras will be helpful in understanding the concepts more efficiently.
Speaker bio
Pushkar Pushp is working as a Data Scientists with WalmartLabs having done his graduation and masters in statistics from ISI, Kolkata. His areas of interests range from pure Mathematics, Python to Computer Vision, Deep Learning. He has extensively work on Keras/tensorflow to develop various state of art models such as Face Recognition,Trigger Word detection ,Machine Translation and other sequence models.
Co-Author
Shivani Naik
I have a Master’s degree in Information Technology with a Data Science major from IIIT Bangalore. Currently, I am working on Computer Vision as a Statistical Analyst at Walmart Labs India. With projects that make use of different ML techniques like object detection, GANs, CNNs, recommendation systems, I have worked with Machine Learning for the past 4 years. I also have a provisionally filed patent titled ‘System and method for produce detection and classification’ for an image classification algorithm.
Slides
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Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more
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