Representation learning with random images
Name
Baradad-mbaradad-SM-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
17.09 MB
Format
Adobe PDF
Checksum (MD5)
7654de9f84c03ab23e14f8cf605724a8
Author(s)
Baradad, Manel
Advisor(s)
Torralba, Antonio
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
Abstract
Current vision systems are trained on huge datasets, and these datasets come with costs: curation is expensive, they inherit human biases, and there are concerns over privacy and usage rights. To counter these costs, interest has surged in learning from cheaper data sources, such as unlabeled images.
In this thesis, we investigate a suite of image generation models that produce images from simple random processes. These are then used as training data for a visual representation learner with a contrastive loss. We study two types of noise processes, statistical image models and deep generative models under different random initializations. Our findings show that it is important for the noise to capture certain structural properties of real data but that good performance can be achieved even with processes that are far from realistic. We also find that diversity is a key property to learn good representations.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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