Improving Image Classification with Generative Adversarial Networks

Generative adversarial networks (GANs) are one of the most promising areas in deep learning research. In this presentation we will briefly introduce GANs and see how they can be used in the real world - specifically for unsupervised learning and synthesizing data sets. We will analyze their strengths, weaknesses, and potential for future break-throughs. uses GANs for a few different tasks in skin cancer detection (image classification), and we will compare the results obtained when using GANs to more standard state of the art deep learning approaches.

Michael Dietz, Founder at Waya.aI

Michael Dietz is focused on applying AI to healthcare with the goal of finding the causes and mechanisms of disease and analyzing the patterns that connect everything together. He is a software engineer and the founder of which is at the intersection of his interests - AI, holistic healthcare and entrepreneurship.

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