Do Better ImageNet Models Transfer Better... for Image Recommendation?

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dc.catalogadorfcr
dc.contributor.authorParra Santander, Denis Alejandro
dc.date.accessioned2024-03-05T15:14:42Z
dc.date.available2024-03-05T15:14:42Z
dc.date.issued2018
dc.description.abstractVisual embeddings from Convolutional Neural Networks (CNN) trained on the ImageNet dataset for the ILSVRC challenge have shown consistently good performance for transfer learning and are widely used in several tasks, including image recommendation. However, some important questions have not yet been answered in order to use these embeddings for a larger scope of recommendation domains: a) Do CNNs that perform better in ImageNet are also better for transfer learning in content-based image recommendation?, b) Does fine-tuning help to improve performance? and c) Which is the best way to perform the fine-tuning? In this paper we compare several CNN models pre-trained with ImageNet to evaluate their transfer learning performance to an artwork image recommendation task. Our results indicate that models with better performance in the ImageNet challenge do not always imply better transfer learning for recommendation tasks (e.g. NASNet vs. ResNet). Our results also show that fine-tuning can be helpful even with a small dataset, but not every fine-tuning works. Our re sults can inform other researchers and practitioners on how to train their CNNs for better transfer learning towards image recommendation systems.
dc.fechaingreso.objetodigital2024-06-06
dc.fuente.origenORCID
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/84008
dc.information.autorucEscuela de Ingeniería; Parra Santander, Denis Alejandro; 0000-0001-9878-8761; 1011554
dc.language.isoen
dc.nota.accesoContenido completo
dc.rightsacceso abierto
dc.titleDo Better ImageNet Models Transfer Better... for Image Recommendation?
dc.typepreprint
sipa.codpersvinculados1011554
sipa.trazabilidadORCID;2024-01-22
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