Source: Engineering Applications of Artificial Intelligence. Unidades: EESC, ICMC
Subjects: TOMADA DE DECISÃO, ANÁLISE DE DESEMPENHO, APRENDIZADO COMPUTACIONAL
ABNT
NAKAMURA, Angelica Tiemi Mizuno; GRASSI JÚNIOR, Valdir; WOLF, Denis Fernando. An effective combination of loss gradients for multi-task learning applied on instance segmentation and depth estimation. Engineering Applications of Artificial Intelligence, Oxford, Elsevier, v. 100, p. 1-10, 2021. Disponível em: < https://doi.org/10.1016/j.engappai.2021.104205 > DOI: 10.1016/j.engappai.2021.104205.APA
Nakamura, A. T. M., Grassi Júnior, V., & Wolf, D. F. (2021). An effective combination of loss gradients for multi-task learning applied on instance segmentation and depth estimation. Engineering Applications of Artificial Intelligence, 100, 1-10. doi:10.1016/j.engappai.2021.104205NLM
Nakamura ATM, Grassi Júnior V, Wolf DF. An effective combination of loss gradients for multi-task learning applied on instance segmentation and depth estimation [Internet]. Engineering Applications of Artificial Intelligence. 2021 ; 100 1-10.Available from: https://doi.org/10.1016/j.engappai.2021.104205Vancouver
Nakamura ATM, Grassi Júnior V, Wolf DF. An effective combination of loss gradients for multi-task learning applied on instance segmentation and depth estimation [Internet]. Engineering Applications of Artificial Intelligence. 2021 ; 100 1-10.Available from: https://doi.org/10.1016/j.engappai.2021.104205