[talks] Riley Simmons-Edler will present his general exam on Friday, May 12, 2017 in CS 401 at 10am.
ngotsis at CS.Princeton.EDU
Mon May 8 10:42:14 EDT 2017
Riley Simmons-Edler will present his general exam on Friday, May 12, 2017 in CS 401 at 10am.
The members of his committee are: Tom Funkhouser, Sebastian Seung, and Szymon Rusinkiewicz.
Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below.
Title: "Self-Supervised and Adversarial Image Intrinsic Decomposition"
Image decomposition into intrinsic components, such as albedo and
lighting, is a highly underconstrained problem. For any given image,
there are infinitely many possible predictions that satisfy the
relationship albedo * lighting = color. However, as humans we can define
a distribution of "plausible" predictions for a given image using a
combination of our internal model of what a valid intrinsic looks like
and the known content of the original image. It is therefore reasonable
to think it is viable to train a deep neural network to do this task by
constraining the output in a similar fashion. Here, I present my work on
using self-supervision and adversarial neural networks to learn an
albedo and lighting decomposition for real photographs, where no ground
truth data exists nor can exist. By using an adversarial network trained
on a distribution of synthetic albedo and lighting, I project a given
color image into the space of possible intrinsics, while using
self-supervision via a reconstruction loss from the intrinsics to the
input image to constrain the predictions to reproduce the input image.
This combination of loss terms makes it possible to learn an accurate
transformation from color input to intrinsic factors without any paired
training data, allowing for training directly on real images and thus
improved performance compared to previous approaches which can only use
synthetic training data.
Textbook: "Deep Learning," Goodfellow, Bengio, and Courville, 2016
Directly Related to my project(methods and previous application works):
"Direct Intrinsics: Learning Albedo-Shading Decomposition by
Convolutional Regression," Narihira, Maire, and Yu, 2015
"DARN: a Deep Adversarial Residual Network for Intrinsic Image
Decomposition," Lettry, Vanhoey, and Van Gool, 2016
"Wasserstein GAN," Arjovsky, Chintala, and Bottou, 2017
"Multi-Scale Context Aggregation by Dilated Convolutions," Yu and
Other Similar Influential Works(in image to image translation):
"Learning from Simulated and Unsupervised Images through Adversarial
Training," Shrivastava et al, 2016
"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks," Zhu, Park, Isola, and Efros, 2017
"Generative Adversarial Nets," Goodfellow et al, 2014
"Imagenet Classification with Deep Convolutional Neural Networks,"
Krizhevsky, Sutskever, and Hinton, 2012
"Backpropagation Applied to Handwritten Zip Code Recognition," LeCun et
al, 1989 (the original convolutional neural networks paper)
More information about the talks