Hadar Averbuch-Elor, Cornell
Monday, September 21, 2020 - 2:30pm
Join Zoom Meeting https://us02web.zoom.us/j/89627764901
Abstract: Deep learning has revolutionized our ability to generate novel images and 3D shapes.
Neural networks are typically trained to map a high-dimensional latent code to full realistic samples. In this talk, I will present two recent works focusing on generation of handwritten text and 3D shapes. In these works, we take a different approach and generate image and shape samples using a more granular part-based decomposition, demonstrating that the whole is not necessarily “greater than the sum of its parts”. I will also discuss how our generation by decomposition approach allows for a semantic manipulation of 3D shapes and improved handwritten text recognition performance.
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