The growing amount of data demands methods that can gradually learn from new samples. However, it is not trivial to continually train a network. Retraining a network with new data usually results in a phenomenon called “catastrophic forgetting”. In a nutshell, the performance of the model on the previous data drops by learning from the new instances. This paper explores this issue in the table detection problem. While there are multiple datasets and sophisticated methods for table detection, the utilization of continual learning techniques in this domain has not been studied. We employed an effective technique called experience replay and performed extensive experiments on several datasets to investigate the effects of catastrophic forgetting. The results show that our proposed approach mitigates the performance drop by 15 percent. To the best of our knowledge, this is the first time that continual learning techniques have been adopted for table detection, and we hope this stands as a baseline for future research.
@article{pub12609,
author = {
Minouei, Mohammad
and
Hashmi, Khurram Azeem
and
Soheili, Mohammad Reza
and
Afzal, Muhammad Zeshan
and
Stricker, Didier
},
editor = {
Egger, Jan
},
title = {Continual Learning for Table Detection in Document Images},
year = {2022},
month = {9},
volume = {12},
number = {18},
pages = {01--16},
journal = {Applied Sciences (MDPI)},
publisher = {MDPI}
}
German Research Center for Artificial Intelligence Deutsches Forschungszentrum für Künstliche Intelligenz
