%0 Book Section %1 Chicco2021 %A Chicco, Davide %B Artificial Neural Networks %C New York, NY %D 2021 %E Cartwright, Hugh %I Springer US %K final thema:Dimensionality_Reduction_by_Learning_an_Invariant_Mapping %P 73--94 %R 10.1007/978-1-0716-0826-5_3 %T Siamese Neural Networks: An Overview %U https://doi.org/10.1007/978-1-0716-0826-5_3 %X Similarity has always been a key aspect in computer science and statistics. Any time two element vectors are compared, many different similarity approaches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correlation coefficient, Spearman's rank correlation coefficient, and others). But if the comparison has to be applied to more complex data samples, with features having different dimensionality and types which might need compression before processing, these measures would be unsuitable. In these cases, a siamese neural network may be the best choice: it consists of two identical artificial neural networks each capable of learning the hidden representation of an input vector. The two neural networks are both feedforward perceptrons, and employ error back-propagation during training; they work parallelly in tandem and compare their outputs at the end, usually through a cosine distance. The output generated by a siamese neural network execution can be considered the semantic similarity between the projected representation of the two input vectors. In this overview we first describe the siamese neural network architecture, and then we outline its main applications in a number of computational fields since its appearance in 1994. Additionally, we list the programming languages, software packages, tutorials, and guides that can be practically used by readers to implement this powerful machine learning model. %@ 978-1-0716-0826-5

@inbook{Chicco2021, abstract = {Similarity has always been a key aspect in computer science and statistics. Any time two element vectors are compared, many different similarity approaches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correlation coefficient, Spearman's rank correlation coefficient, and others). But if the comparison has to be applied to more complex data samples, with features having different dimensionality and types which might need compression before processing, these measures would be unsuitable. In these cases, a siamese neural network may be the best choice: it consists of two identical artificial neural networks each capable of learning the hidden representation of an input vector. The two neural networks are both feedforward perceptrons, and employ error back-propagation during training; they work parallelly in tandem and compare their outputs at the end, usually through a cosine distance. The output generated by a siamese neural network execution can be considered the semantic similarity between the projected representation of the two input vectors. In this overview we first describe the siamese neural network architecture, and then we outline its main applications in a number of computational fields since its appearance in 1994. Additionally, we list the programming languages, software packages, tutorials, and guides that can be practically used by readers to implement this powerful machine learning model.}, added-at = {2020-12-07T10:39:29.000+0100}, address = {New York, NY}, author = {Chicco, Davide}, biburl = {https://www.bibsonomy.org/bibtex/2c162f0afb9160f3b0a55f823862fa923/fabiansieper}, booktitle = {Artificial Neural Networks}, description = {Siamese Neural Networks: An Overview | SpringerLink}, doi = {10.1007/978-1-0716-0826-5_3}, editor = {Cartwright, Hugh}, interhash = {1eced809e8de90952964b0abed42a92f}, intrahash = {c162f0afb9160f3b0a55f823862fa923}, isbn = {978-1-0716-0826-5}, keywords = {final thema:Dimensionality_Reduction_by_Learning_an_Invariant_Mapping}, pages = {73--94}, publisher = {Springer US}, timestamp = {2020-12-07T10:46:54.000+0100}, title = {Siamese Neural Networks: An Overview}, url = {https://doi.org/10.1007/978-1-0716-0826-5_3}, year = 2021 }