Domain-specific self-supervised learning
Recently, contrastive self-supervised feature learning has almost closed the gap to supervised feature learning in generic image collections [1,2]. In special domain, often there is insufficient labelled data available to conduct supervised pre-training. Here it seems natural to rely on self-supervised techniques. The task in this thesis will be to investigate how contrastive self-supervised techniques can be applied in specific domains.
Potential domains are satellite images, medical images and video recordings of animal behaviour.
 T. Chen et al.: A simple framework for contrastive learning of visual representations
 X. Chen et al.: Improved baselines with momentum contrastive learning
- Good mathematical understanding (in particular statistics and linear algebra)
- Python programming
- Experience in machine learning