Revisiting activation functions: empirical evaluation for image understanding and classification
Author:
Funder
DST, MST, GoI
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16159-2.pdf
Reference44 articles.
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2. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495
3. Bawa VS, Kumar V (2019) Linearized sigmoidal activation: A novel activation function with tractable non-linear characteristics to boost representation capability. Expert Syst Appl 120:346–356
4. Bozkurt F (2022) Skin lesion classification on dermatoscopic images using effective data augmentation and pre-trained deep learning approach. Multimed Tools Appl 82(12):18985–19003. https://link.springer.com/article/10.1007/s11042-022-14095-1
5. Chui CK, Li X (1992) Approximation by ridge functions and neural networks with one hidden layer. J Approx Theory 70(2):131–141
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