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Improving the Training of Deep Convolutional Neural Networks for Art Classification: from Transfer Learning to Multi-Task Learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sabatelli, Matthia Kestemont, Mike Geurts, Pierre |
| Copyright Year | 2019 |
| Abstract | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://2019.dhbenelux.org/wp-content/uploads/sites/13/2019/08/DH_Benelux_2019_paper_17.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
