Learned Data Augmentation for Bias Correction

Pola Elisabeth Schwöbel

Research output: Book/ReportPh.D. thesis

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This thesis consists of three independent pieces of research that can be divided into two subject groups. The first block of topics is invariance learning and learned data augmentation (Paper 1 and 2 presented in Chapter 3 and 4, respectively). Paper 1 is concerned with learning invariances (or equivalently, as we will see, data augmentation) via Bayesian model selection and the marginal likelihood. In Paper 2, we take a different approach: achieving invariance by automatically pose-normalising inputs. The second topic block is fairness in machine learning which we cover in Paper 3 (Chapter 6). In addition to published research, this thesis contains the following original material. The first two chapters introduce the topics and Chapter 5 connects data augmentation with fairness. It investigates whether data augmentation and upsampling can be used make datasets more balanced, and, by correcting data bias, making models more fair. Chapter 7 concludes the work with a summary and discussion.
Original languageEnglish
PublisherTechnical University of Denmark
Number of pages127
Publication statusPublished - 2022


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