Anticausal Learning for Inverse Problems and its Application on Optoacoustic Tomography
Artificial intelligence algorithms commonly exhibit poor performance when deployed on data whose distribution deviates from the one utilized during the training phase. While this vulnerability can be addressed post-training, doing so may necessitate a computationally intensive fine-tuning process an...
Guardado en:
| Autores principales: | Vera, Matías, González, Martín Germán, Rey Vega, Leonardo |
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| Formato: | Artículo publishedVersion |
| Lenguaje: | Español |
| Publicado: |
FIUBA
2025
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| Materias: | |
| Acceso en línea: | https://elektron.fi.uba.ar/elektron/article/view/222 https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=elektron&d=222_oai |
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