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Christopher Beckham

Alumni

Publications

Adversarial Mixup Resynthesizers
Christopher Beckham
Sina Honari
Alex Lamb
Vikas Verma
Farnoosh Ghadiri
R Devon Hjelm
Christopher Pal
In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders. We explore mod… (voir plus)els that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.
2018-12-31
DGS@ICLR (publié)
openreview.net
openreview.net
Deep Learning recognizes weather and climate patterns
Karthik Kashinath
M. Prabhat
Mayur Mudigonda
Ankur Mahesh
Sookyung Kim
Yunjie Liu
S Ebrahimi Kahou
B. Toms
Evan Racah
Christopher Beckham
Christopher Pal
Tegan Maharaj
Jim Biard
K. Kunkel
Dean Nesbit Williams
Travis O'Brien
M. Wehner
W. Collins
2018-11-30
(publié)
www.semanticscholar.org
Unsupervised Depth Estimation, 3D Face Rotation and Replacement
Joel Ruben Antony Moniz
Christopher Beckham
Simon Rajotte
Sina Honari
Christopher Pal
We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicti… (voir plus)ng 3D viewpoint transformations that match a desired pose and facial geometry. We achieve this by inferring the depth of facial keypoints of an input image in an unsupervised manner, without using any form of ground-truth depth information. We show how it is possible to use these depths as intermediate computations within a new backpropable loss to predict the parameters of a 3D affine transformation matrix that maps inferred 3D keypoints of an input face to the corresponding 2D keypoints on a desired target facial geometry or pose. Our resulting approach, called DepthNets, can therefore be used to infer plausible 3D transformations from one face pose to another, allowing faces to be frontalized, transformed into 3D models or even warped to another pose and facial geometry. Lastly, we identify certain shortcomings with our formulation, and explore adversarial image translation techniques as a post-processing step to re-synthesize complete head shots for faces re-targeted to different poses or identities.
2017-12-31
Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (publié)
arxiv.org
arxiv.org
Unimodal Probability Distributions for Deep Ordinal Classification
Christopher Beckham
Christopher Pal
Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propos… (voir plus)e a straightforward technique to constrain discrete ordinal probability distributions to be unimodal via a combination of the Poisson probability mass function and the softmax nonlinearity. We evaluate this approach on two large ordinal image datasets and obtain promising results.
2017-07-16
Proceedings of the 34th International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press