Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks

Summary (the publisher’s abstract is not openly available): A book on unsupervised visual learning in space and time. It proposes a general unsupervised learning model that brings together graphs and deep neural networks, covering unsupervised graph and hypergraph matching and clustering, feature selection that meets unsupervised learning, object segmentation in video from highly probable positive features, coupling appearance and motion through space and time, and learning over several generations of teacher and student networks — from pixels to seeing. Reported by BookAuthority as the best-selling book on unsupervised learning since 2020.

@book{leordeanu2020unsupervised,
  title = {Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks},
  author = {Marius Leordeanu},
  year = {2020},
  howpublished = {Springer},
}