349 citations · 419 across the 5 of their papers we have counts for
6 papers
Semantic Segmentation for Thermal Images: A Comparative Survey
Zülfiye Kütük, Görkem Algan
Semantic segmentation is a challenging task since it requires excessively more low-level spatial information of the image compared to other computer vision problems. The accuracy o…
MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels
Görkem Algan, Ilkay Ulusoy
Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs). In order to address this problem, we propose a label noise…
Deep Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approach
Görkem Algan, Ilkay Ulusoy, Şaban Gönül +2
Computer vision systems recently made a big leap thanks to deep neural networks. However, these systems require correctly labeled large datasets in order to be trained properly, wh…
Meta Soft Label Generation for Noisy Labels
Görkem Algan, Ilkay Ulusoy
The existence of noisy labels in the dataset causes significant performance degradation for deep neural networks (DNNs). To address this problem, we propose a Meta Soft Label Gener…
Label Noise Types and Their Effects on Deep Learning
Görkem Algan, İlkay Ulusoy
The recent success of deep learning is mostly due to the availability of big datasets with clean annotations. However, gathering a cleanly annotated dataset is not always feasible…
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
Görkem Algan, Ilkay Ulusoy
Image classification systems recently made a giant leap with the advancement of deep neural networks. However, these systems require an excessive amount of labeled data to be adequ…