Towards Uncertainty-Aware Disentangled Representations

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2021-9-09

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Özyeğin, Sezai Artun

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In many computer vision tasks, not every part of an object of interest is always visible because of challenges like occlusion, viewpoint and pose variation. One approach to these kinds of challenges is separating the representation so that they would correspond to different regions. In this thesis, we tackle the problem of obtaining disentangled representations while estimating the uncertainty of each factor to assess its availability. Representations are disentangled using a factor-related supervised task and by using an adversarial loss, unrelated information is removed. Uncertainty of factors are estimated using loss attenuation over the same factor-related task. We try several methods to integrate uncertainty values into both the training procedure and the decision making process during test time to make the model more robust to unavailable parts. The experiments are conducted over a toy dataset and the person re-identification task (namely, the Market-1501 dataset) which can benefit from disentangled representations.

Subject Keywords

Disentanglement, Uncertainty Estimation, Representation Learning, Deep Learning

URI

https://hdl.handle.net/11511/93206

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

S. A. Özyeğin, “Towards Uncertainty-Aware Disentangled Representations,” M.S. - Master of Science, Middle East Technical University, 2021.