[AI Seminar] AI Lunch -- Hsiao-Yu Tung -- April 4

[Adams Wei Yu]

Dear faculty and students,

We look forward to seeing you Next Tuesday, April 4, at noon in NSH 3305
for AI lunch. To learn more about the seminar and lunch, please visit the AI
 Lunch webpage <http://www.cs.cmu.edu/~aiseminar/>.

On Tuesday, Hsiao-Yu Tung <https://sfish0101.bitbucket.io/> will give the
following talk:

Title: Adversarial Inversion: Self-supervision with Adversarial Priors.

Abstract:

We as humans form explanations of visual observations in terms of familiar
concepts and memories that are used to interpret and complete information
of the image pixels. Computer Vision researchers have developed excellent
methods that learn a direct  mapping  from  images  to  desired outputs
using human annotations or synthetically generated data. Despite their
 success,  such  supervised  models  very  much  depend on the amount of
annotated data available, a gap we seek to address.

In this talks, we introduce adversarial inversion, a weakly supervised
neural network model that combines self-supervision with adversarial
constraints. Given visual input, our model first generates a set of
desirable intermediate latent variables, which we call “imaginations”,
e.g.,  3D pose and camera viewpoint, such that these imagination matches
what we observe. Adversarial inversion can be trained with or without
paired supervision of standard supervised models, as it does not require
paired annotations.  It can instead exploit a large number of unlabelled
 images. We  empirically  show  adversarial  inversion outperforms previous
state-of-the-art supervised models on 3D human pose estimation and 3D scene
depth estimation. Further, we show interesting results on biased image
editing.


Joint work with Adam Harley, William Seto and Katerina Fragkiadaki.
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