Deep Convolutional Neural Networks; Transfer Learning; Multi-Task Learning
Abstract :
[en] Deep Convolutional Neural Networks have become the most popular algorithm for Computer
Vision (CV) problems. However, they are well known to be particularly hard to train. A large
set of possible hyperparameters, combined with the need for large amounts of training data,
can put serious constraints on their optimization procedure. In this paper, we explore several
strategies which can facilitate their training when classifying images representing heritage
objects, a CV area which is relatively unexplored.
Disciplines :
Computer science
Author, co-author :
Sabatelli, Matthia ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Kestemont, Mike; Universiteit Antwerpen, Belgium > Antwerp Center for Digital Humanities and Literary Criticism (ACDC)
Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Language :
English
Title :
Improving the Training of Deep Convolutional Neural Networks for Art Classification: from Transfer Learning to Multi-Task Learning
Publication date :
October 2019
Event name :
The 6th Digital Humanities (DH) Benelux Conference
