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| Hauptseite > Publikationsdatenbank > Enhancing Large Batch Size Training of Deep Models for Remote Sensing Applications > print |
| Hauptseite > Publikationsdatenbank > Enhancing Large Batch Size Training of Deep Models for Remote Sensing Applications > print |
| 001 | 893825 | ||
| 005 | 20250310131243.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS47720.2021.9555136 |2 doi |
| 024 | 7 | _ | |a WOS:001250139801213 |2 WOS |
| 037 | _ | _ | |a FZJ-2021-02864 |
| 100 | 1 | _ | |a Sedona, Rocco |0 P:(DE-Juel1)178695 |b 0 |
| 111 | 2 | _ | |a IEEE International Geoscience and Remote Sensing Symposium |g IGARSS 2021 |c Brussels |d 2021-07-12 - 2021-07-16 |w Belgium |
| 245 | _ | _ | |a Enhancing Large Batch Size Training of Deep Models for Remote Sensing Applications |
| 260 | _ | _ | |c 2021 |b IEEE |
| 295 | 1 | 0 | |a 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS : [Proceedings] - IEEE, 2021. - ISBN 978-1-6654-0369-6 - doi:10.1109/IGARSS47720.2021.9555136 |
| 300 | _ | _ | |a 1583-1586 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1635430213_13275 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 520 | _ | _ | |a A wide variety of Remote Sensing (RS) missions arecontinuously acquiring a large volume of data every day. The availability of large datasets has propelled Deep Learning (DL) methods also in the RS domain. Convolutional Neural Networks (CNNs) have become the state of the art when tackling the classification of images, however the process of training is time consuming. In this work we exploit the Layer-wise Adaptive Moments optimizer for Batch training (LAMB) optimizer to use large batch size training on High-Performance Computing (HPC) systems. With the use of LAMB combined with learning rate scheduling and warm-up strategies, the experimental results on RS data classification demonstrate that a ResNet50 can be trained faster with batch sizes up to 32K. |
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| 588 | _ | _ | |a Dataset connected to CrossRef Conference |
| 700 | 1 | _ | |a Cavallaro, Gabriele |0 P:(DE-Juel1)171343 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Riedel, Morris |0 P:(DE-Juel1)132239 |b 2 |
| 700 | 1 | _ | |a Book, Matthias |0 P:(DE-HGF)0 |b 3 |
| 773 | _ | _ | |a 10.1109/IGARSS47720.2021.9555136 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/893825/files/IGARSS2021_SAT6.pdf |y Restricted |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2021 |
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| 980 | _ | _ | |a VDB |
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