Object discovery via layer disposal
Author(s)
Oktay, Deniz, M. Eng. Massachusetts Institute of Technology
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Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Antonio Torralba.
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A key limitation of semantic image segmentation approaches is that they require large amounts of densely labeled training data. In this thesis, we introduce a method to learn to segment images with unlabeled data. The intuition behind the approach is that removing objects from images will yield natural images, however removing random patches will yield unnatural images. We capitalize on this signal to develop an auto-encoder that decomposes an image into layers, and when all layers are combined, it reconstructs the input image. However, when a layer is removed, the model learns to produce a different image that still looks natural to an adversary, which is possible by removing objects. Experiments and visualizations suggest that this model automatically learns to segment objects in images better than baselines. Some parts of this thesis represent joint work with Dr. Carl Vondrick and Professor Antonio Torralba.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 51-54).
Date issued
2017Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.