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cs.LG2024
On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods
Hariprasath Govindarajan, Per Sidén, Jacob Roll +1
A prominent self-supervised learning paradigm is to model the representations as clusters, or more generally as a mixture model. Learning to map the data samples to compact represe…
cs.LG2024
Prior Learning in Introspective VAEs
Ioannis Athanasiadis, Fredrik Lindsten, Michael Felsberg
Variational Autoencoders (VAEs) are a popular framework for unsupervised learning and data generation. A plethora of methods have been proposed focusing on improving VAEs, with the…