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document
Sharma, Anirvin (author)
Image data augmentation has been regarded as a reliable and effective way to increase the data available for training. With the advent and rise of Generative AI, generative data augmentation has been shown to realize even better gains in performance for downstream tasks. However, these performance gains are often the cause of "extra information"...
master thesis 2023
document
Sharma, Agrim (author)
Traditionally, convolutional neural networks are feedforward networks with a deep and complex hierarchy. Conversely, the human brain has a relatively shallow hierarchy with recurrent connections. Replicating this recurrence may allow for shallower and easier to understand computer vision models that may possess characteristics usually attributed...
master thesis 2022
document
Pintea, S. (author), Sharma, S. (author), Vossepoel, F.C. (author), van Gemert, J.C. (author), Loog, M. (author), Verschuur, D.J. (author)
This article investigates bypassing the inversion steps involved in a standard litho-type classification pipeline and performing the litho-type classification directly from imaged seismic data. We consider a set of deep learning methods that map the seismic data directly into litho-type classes, trained on two variants of synthetic seismic data:...
journal article 2021