Contribution to a conference proceedings

FZJ-2022-03638

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Accelerating Hyperparameter Tuning of a Deep Learning Model for Remote Sensing Image Classification

Aach, M. (Corresponding author)FZJ* ; Sedona, R.FZJ* ; Lintermann, A.FZJ* ; Cavallaro, G.FZJ* ; Neukirchen, H. ; Riedel, M.FZJ*

2022
IEEE

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Please use a persistent id in citations: http://hdl.handle.net/2128/32035  doi:10.1109/IGARSS46834.2022.9883257

Abstract: Deep Learning models have proven necessary in dealing with the challenges posed by the continuous growth of data volume acquired from satellites and the increasing complexity of new Remote Sensing applications. To obtain the best performance from such models, it is necessary to fine-tune their hyperparameters. Since the models might have massive amounts of parameters that need to be tuned, this process requires many computational resources. In this work, a method to accelerate hyperparameter optimization on a High-Performance Computing system is proposed. The data batch size is increased during the training, leading to a more efficient execution on Graphics Processing Units. The experimental results confirm that this method reduces the runtime of the hyperparameter optimization step by a factor of 3 while achieving the same validation accuracy as a standard training procedure with a fixed batch size.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. RAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733) (951733)
3. PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) (PHD-NO-GRANT-20170405)

Appears in the scientific report 2022

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Database coverage:

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