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Self-Supervised Learning for Semantic Segmentation of Images

Date

2023-09-22

Journal Title

Journal ISSN

Volume Title

Publisher

ORCID

0009-0009-4459-5257

Type

Thesis

Degree Level

Masters

Abstract

Artificial Neural Networks (ANN) are powerful Machine Learning (ML) models that can help solve problems that are hard or even impossible to design solutions for by hand. These models learn to exploit information present in their target datasets to solve various problems. However, labelling data can be quite expensive, time-consuming and often requires a domain expert. Therefore it would be quite beneficial if one could train a model in such a way that exploits unlabeled data. Fortunately, Self-Supervised Learning (SSL) methods are a family of learning algorithms that attempt to do just that. Many SSL methods exist, but in this thesis, we explore Barlow Twins (BT) — a siamese network based on redundancy reduction, and Image Reconstruction (IR) — a method proposed in Karnam’s thesis. In addition, we extend the Image Reconstruction method with both Coarse Cutout and Hide-and-Seek augmentations as they have been applied in similar supervised and weakly-supervised segmentation task scenarios. We apply these methods and investigate the results with the PASCAL VOC dataset.

Description

Keywords

Neural Networks, Machine Learning, Self-Supervised Learning, Semantic Segmentation

Citation

Degree

Master of Science (M.Sc.)

Department

Computer Science

Program

Computer Science

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DOI

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