RT Conference Proceedings
T1 Gait recognition applying Incremental learning
A1 Castro, Francisco M.
A1 Marín-Jiménez, Manuel J.
A1 Guil-Mata, Nicolás
A1 Schmid, Cordelia
A1 Alahari, Karteek
K1 Reconocimiento óptico de formas (Informática)
K1 Redes neuronales (Informática)
AB when new knowledge needs to be included in a classifier, the model is retrained from scratch using a huge training set that contains all available information of both old and new knowledge. However, in this talk, we present a way to include new information in a previously trained model without training from scratch and using a small subset of old data. We perform a thorough experimental evaluation of the proposed approach on two image classification datasets: CIFAR-100 and ImageNet. The experiment results show that it is possible to include new knowledge in a model without forgetting the previous one, although, the performance is still lower than training from scratch with the complete training set.
YR 2019
FD 2019-11-25
LK https://hdl.handle.net/10630/18907
UL https://hdl.handle.net/10630/18907
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026