Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack (2021)
- Authors:
- USP affiliated authors: PONTI, MOACIR ANTONELLI - ICMC ; CAVALLARI, GABRIEL BISCARO - ICMC
- Unidade: ICMC
- DOI: 10.1109/SIBGRAPI54419.2021.00038
- Subjects: APRENDIZADO COMPUTACIONAL; RECONHECIMENTO DE IMAGEM
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2021
- Source:
- Título: Proceedings
- Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
CAVALLARI, Gabriel Biscaro e PONTI, Moacir Antonelli. Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack. 2021, Anais.. Los Alamitos: IEEE, 2021. Disponível em: https://doi.org/10.1109/SIBGRAPI54419.2021.00038. Acesso em: 09 abr. 2026. -
APA
Cavallari, G. B., & Ponti, M. A. (2021). Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI54419.2021.00038 -
NLM
Cavallari GB, Ponti MA. Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack [Internet]. Proceedings. 2021 ;[citado 2026 abr. 09 ] Available from: https://doi.org/10.1109/SIBGRAPI54419.2021.00038 -
Vancouver
Cavallari GB, Ponti MA. Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack [Internet]. Proceedings. 2021 ;[citado 2026 abr. 09 ] Available from: https://doi.org/10.1109/SIBGRAPI54419.2021.00038 - Estudo de representações de imagens de múltiplos domínios a partir de aprendizado profundo não supervisionado e semi-supervisionado
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