LIDAV
Code for learning local image descriptors with autoencoedrs and variational autoencoders
Description:
Repository for reproducing results of our paper “Efficient Local Image Descriptors Learned with Autoencoders” that is currently under review in IEEE Access. It contains code for training autoencoders and variational autoencoders, performing a hyperparameter sweep over different parameters as described in the paper, and for automatically evaluating the descriptor’s performance, both based on the established HPatches benchmark and based on our proposed approximate evaluation method.
More Information:
- Grand Challenge/Workpackage(s): GC1 - WP4
- Responsible Research Lead: Aleksandra Pizurica
- Authors: Nina Zizakic
- Programming Language: Python
- Link to the repository
- Categories: Un/Semi-supervised learning, Images
Publications:
- N. Zizakic and A. Pizurica, "Efficient Local Image Descriptors Learned with Autoencoders", IEEE Access, 2021, (Submitted)
