Few-Shot Remote Sensing Classification with Meta-Learning: Challenges and Opportunities
![Few-Shot Remote Sensing Classification with Meta-Learning: Challenges and Opportunities - Available at University of Iceland](https://english.hi.is/sites/default/files/styles/efsta_mynd___vi_bur_um/public/stjori/bakgrunnsmyndir2/1920_KRI_byggingar_200507_015.jpg?itok=OjupZFGz)
Surbhi Sharma, PhD student in Computational Engineering at the University of Iceland and the Jülich Supercomputing Centre (JSC), Germany gives the lecture Few-Shot Remote Sensing Classification with Meta-Learning: Challenges and Opportunities.
Event on Zoom:
https://eu01web.zoom.us/my/crs.talks.2022
Meeting ID: 354 191 1354
Abstract:
Obtaining high generalization and classification accuracies with limited availability of annotated data and from data having diverse distributions is often challenging in the field of remote sensing.
Few-shot learning with the help of meta-learning can tackle meta-problems where learning algorithm learns to learn new tasks or classes from limited training data, thereby decreasing the learning time with improved generalization.
Surbhi Sharma
![Few-Shot Remote Sensing Classification with Meta-Learning: Challenges and Opportunities](https://english.hi.is/sites/default/files/styles/mynd___kassa_fyrir_fr_ttir_/public/vidburdir/p24j3.jpg?itok=vhJJcLiy)