@inproceedings{6fcdae65c2f54972b9e778eabd054f9a,
title = "Automatic estimation of dog age: The dogage dataset and challenge",
abstract = "Automatic age estimation is a challenging problem attracting attention of the computer vision and pattern recognition communities due to its many practical applications. Artificial neural networks, such as CNNs are a popular tool for tackling this problem, and several datasets which can be used for training models are available. Despite the fact that dogs are the most well studied species in animal science, and that ageing processes in dogs are in many aspects similar to those of humans, the problem of age estimation for dogs has so far been overlooked. In this paper we present the DogAge dataset and an associated challenge, hoping to spark the interest of the scientific community in the yet unexplored problem of automatic dog age estimation.",
keywords = "Age estimation, Applications of deep learning, CNN, Computer vision",
author = "Anna Zamansky and Sinitca, {Aleksandr M.} and Kaplun, {Dmitry I.} and Dutra, {Luisa M.L.} and Young, {Robert J.}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 28th International Conference on Artificial Neural Networks, ICANN 2019 ; Conference date: 17-09-2019 Through 19-09-2019",
year = "2019",
doi = "10.1007/978-3-030-30508-6_34",
language = "American English",
isbn = "9783030305079",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "421--426",
editor = "Tetko, {Igor V.} and Pavel Karpov and Fabian Theis and Vera Kurkov{\'a}",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2019",
address = "Germany",
}