Practical data efficient deep learning trough contrastive self-supervised learning
At a glance
- Project leader : Lukas Tuggener
- Project budget : CHF 60'480
- Project status : ongoing
- Funding partner : Public sector (excl. federal government)
- Project partner : Universität Zürich / Neural Learning and Intelligent Systems Group
- Contact person : Lukas Tuggener
Description
Deep Learning is the key building block of most modern AI systems, but its data hunger is a problem - especially from an applied perspective. The goal of this project is to enable data efficient practical deep learning by developing novel contrastive learning methods.