Exploring Unsupervised Learning for Computer Vision Tasks with Neural Networks KU Leuven
Traditional supervised learning algorithms for computer vision tasks usually rely on large extensively annotated datasets. However, this labeling process can be expensive, biased, and susceptible to ambiguity.
The thesis explores self-supervised or unsupervised learning as a viable alternative to overcome these obstacles.
Specifically, it tackles fundamental perception tasks via neural networks and aims to automatically discover ...