Zelf-gesuperviseerd Leren van Visuele Representaties door gebruik te maken van 2D Structuur en het Bootstrappen van Representaties

While supervised deep learning has revolutionized computer vision, its reliance on labeled datasets presents critical challenges, including high annotation costs, inherent biases, and limited transferability of learned representations. Self-supervised learning is a compelling alternative, where meaningful representations are learned directly from unannotated data. By leveraging structural patterns and correlations, self-supervised methods unlock the potential to create more adaptable, scalable, and unbiased learning algorithms. This thesis introduces novel approaches to improve self-supervised learning by extracting richer supervision signals from raw data. Specifically, it explores two key strategies in the context of visual data: (1) leveraging spatial structure and (2) bootstrapping past representations, hence the thesis subtitle. Broadly, machine learning pipelines consist of three fundamental components: the dataset, the hypothesis class, and the learning algorithm. While improvements can be made to any of these elements, this manuscript focuses exclusively on the learning algorithm, assuming a fixed dataset and hypothesis class. Each chapter and corresponding appendix present a new learning algorithm, varying both the data combinations used for self-supervision and the granularity at which self-supervision is applied.