A Comparison of Different Loss Computations in Siamese Networks for Authorship Verification
Citation
GÜLCÜ, Ayla, İsmail Taha SAMED & Osman Furkan KARAKUŞ. "A Comparison of Different Loss Computations in Siamese Networks for Authorship Verification". A Comparison of Different Loss Computations, 460. (2023): 402-413.Abstract
In this study, we consider the author verification problem as a binary
classification problem where the aim is to identify whether the two inputs belong to
the same author or not. For this task, we have used several Siamese convolutional
neural networks-based models. The first model employs a Siamese network which
is trained using binary cross-entropy loss after the absolute distance computation.
In addition to this baseline model, we have implemented another model which
employs a concatenation operation. Moreover, two contrastive loss-based models
have also been implemented for the same task. Two publicly available benchmark
datasets, IAM and CVL, have been used in the study. Training, validation and
test datasets that have been used to train and test each of those models have been
generated from those datasets. Experimental results show that the performances
of the two cross-entropy loss-based models are similar and much better than that
of the contrastive loss-based models.