Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis (2018)
- Authors:
- Autor USP: PONTI, MOACIR ANTONELLI - ICMC
- Unidade: ICMC
- DOI: 10.1109/SIBGRAPI.2018.00063
- Subjects: APRENDIZADO COMPUTACIONAL; PROCESSAMENTO DE IMAGENS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2018
- Source:
- Título do periódico: Proceedings
- ISSN: 2377-5416
- Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
CAVALLARI, Gabriel B e RIBEIRO, Leonardo Sampaio F e PONTI, Moacir Antonelli. Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis. 2018, Anais.. Los Alamitos: IEEE, 2018. Disponível em: https://doi.org/10.1109/SIBGRAPI.2018.00063. Acesso em: 04 maio 2024. -
APA
Cavallari, G. B., Ribeiro, L. S. F., & Ponti, M. A. (2018). Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SIBGRAPI.2018.00063 -
NLM
Cavallari GB, Ribeiro LSF, Ponti MA. Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis [Internet]. Proceedings. 2018 ;[citado 2024 maio 04 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00063 -
Vancouver
Cavallari GB, Ribeiro LSF, Ponti MA. Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis [Internet]. Proceedings. 2018 ;[citado 2024 maio 04 ] Available from: https://doi.org/10.1109/SIBGRAPI.2018.00063 - Does background intensity estimation influence the iterative restoration of microscope images?
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Informações sobre o DOI: 10.1109/SIBGRAPI.2018.00063 (Fonte: oaDOI API)
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