Deep learning of robust representations for multi-instance and multi-label image classification
Pre-Publicación
2020
Corporación Universidad de la Costa
In multi-instance problems (MIL), an arbitrary number of instances is associated with a class label. Therefore, the labeling of training data becomes simpler (since it is done together, instead of individually) with the disadvantage that a weakly supervised database is produced [9]. In the PCRY, each restaurant is represented by a set of images that share the attribute label(s) of that establishment. This paper explores the use of previously learned attribute extractors, trained in 3 different databases that are similar and complementary to the PCRY database
- Artículos científicos [3120]
Descripción:
DEEP LEARNING OF ROBUST REPRESENTATIONS FOR MULTI-INSTANCE AND MULTILABEL IMAGE CLASSIFICATION.pdf
Título: DEEP LEARNING OF ROBUST REPRESENTATIONS FOR MULTI-INSTANCE AND MULTILABEL IMAGE CLASSIFICATION.pdf
Tamaño: 5.890Kb
PDFLEER EN FLIP
Título: DEEP LEARNING OF ROBUST REPRESENTATIONS FOR MULTI-INSTANCE AND MULTILABEL IMAGE CLASSIFICATION.pdf
Tamaño: 5.890Kb
PDFLEER EN FLIP
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