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000890968 1001_ $$0P:(DE-Juel1)178695$$aSedona, Rocco$$b0$$eCorresponding author$$ufzj
000890968 1112_ $$a2020 IEEE International Geoscience and Remote Sensing Symposium$$cOnline event$$d2020-09-26 - 2020-10-02$$gIGARSS 2020$$wHawaii
000890968 245__ $$aScaling Up a Multispectral Resnet-50 to 128 GPUs
000890968 260__ $$bIEEE$$c2020
000890968 300__ $$a1058 - 1061
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000890968 520__ $$aSimilarly to other scientific domains, Deep Learning (DL)holds great promises to fulfil the challenging needs of RemoteSensing (RS) applications. However, the increase in volume,variety and complexity of acquisitions that are carried outon a daily basis by Earth Observation (EO) missions generatesnew processing and storage challenges within operationalprocessing pipelines. The aim of this work is to show thatHigh-Performance Computing (HPC) systems can speed upthe training time of Convolutional Neural Networks (CNNs).Particular attention is put on the monitoring of the classificationaccuracy that usually degrades when using large batchsizes. The experimental results of this work show that thetraining of the model scales up to a batch size of 8,000, obtainingclassification performances in terms of accuracy in linewith those using smaller batch sizes.
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000890968 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b1$$ufzj
000890968 7001_ $$0P:(DE-Juel1)158080$$aJitsev, Jenia$$b2$$ufzj
000890968 7001_ $$0P:(DE-Juel1)140202$$aStrube, Alexandre$$b3$$ufzj
000890968 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b4$$ufzj
000890968 7001_ $$0P:(DE-HGF)0$$aBook, Matthias$$b5
000890968 773__ $$a10.1109/IGARSS39084.2020.9324237
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000890968 9141_ $$y2021
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