In the field of image recognition many feature attribution methods have been proposed with the purpose of explaining a model's behavior using visual cues.
In this work we introduce the MAMe dataset, an image classification dataset with remarkable high resolution and variable shape properties.
Ranked #1 on Image Classification on MAMe
Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development.
In sight of the increasing importance of problems that can benefit from exploiting high-resolution (HR) and variable-shape, and with the goal of promoting research in that direction, we introduce a new family of datasets (MetH).
Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes.
In this paper we evaluate the impact of using the Full-Network embedding in this setting, replacing the original image representation in a competitive multimodal embedding generation scheme.
Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option.
We introduce a community detection algorithm (Fluid Communities) based on the idea of fluids interacting in an environment, expanding and contracting as a result of that interaction.
Data Structures and Algorithms Social and Information Networks Physics and Society
We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning.