DataHour: Diffusion Models for Generative Arts

DataHour: Diffusion Models for Generative Arts

21 Oct 202213:10pm - 21 Oct 202214:10pm

DataHour: Diffusion Models for Generative Arts

About the Event

Diffusion models form a separate class of Adversarial Networks aimed at diffusing different latent data points through a space in order to create a different latent space. Generally traditional GAN variants like DCGAN/CycleGAN etc are predominantly used for generating different latent spaces, diffusion however is a dual step process which tries to merge different latent spaces to create an entirely new one.

In this DataHour, Abhilash will explain about the flow of generative latent state representation from GANs, VAEs to Diffusion methods. Since Diffusions are based on the Markov model, he will be building small diffusion models to make you understand latent space representation from images. He will also demonstrate to you how to analyze contemporary multimodal models such as Dall-e/CLIP/unCLIP/GLIDE/Imagen in the context of Diffusion models to replicate and create "Generative AI" which is taking the NFT world by storm.

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Who is this DataHour for?

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About the Speaker

Abhilash Majumder

Abhilash Majumder

 Senior SuperComputing Engineer at Intel

Abhilash Majumder is currently working as a senior SuperComputing Engineer at Intel enabling next generation GPUs for exa scale computing, deep learning of very large models spanning vision and multimodal contexts. He was a research scientist for Morgan Stanley and a collaborator with Imperial College London for finetuning and building large language models . Prior to that, he was a part of HSBC working on knowledge graphs, semantic bots and transformers, part of Unity Technologies (ML Agents and Reinforcement Learning) ; and a part of Google Research for the Albert model. He is also an author, mentor and have provided Deep Learning/Quantum Deep Learning related sessions at Python conferences globally. 

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