Abstract: A CycleGAN is a variant of the generative adversarial network (GAN) architecture designed to handle unpaired image conversion problems. In this talk, Tanishq will describe what are CycleGANs, what applications they are best suited for, and their pitfalls. Additionally, he will walk through code for model training and inference as part of a code demo. Finally, he will present his own research applying CycleGANs (and related models) to pathology and microscopy.
Bio: Tanishq Abraham is considered a child genius and a prodigy. He graduated high school at 10 years old with a 4.0 GPA and at 11 he obtained 3 college Associate Degrees also with a perfect 4.0 GPA. At 14, he graduated from University of California, Davis as a biomedical engineer with summa cum laude. As an undergrad, he has presented papers at several conferences. At 14, he was the first author on a review paper about smart bio-inspired vesicles and it’s biomedical engineering applications published in IOP Physical Biology. At 16, he is a published book chapter author in the book “Artificial Intelligence and Deep Learning in Pathology”. Now at 17, he is a 2nd year PhD student in biomedical engineering at UC Davis. He has worked for a year in the Levenson lab at UC Davis on applying deep learning to novel microscopy techniques in order to enable digital pathology applications. His recent conference was presenting his PhD research at the ICML2020 Computational Biology workshop. Tanishq has also reviewed few books that includes two science fiction books and recently a machine learning book.
Follow him on Twitter: https://twitter.com/iScienceLuvr
#MachineLearning #DeepLearning #GAN