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Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?
Our findings can provide an indication for the development of robust landmark detection systems in medical images when no large annotated dataset is available.
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods
In this paper, focusing on image classification, we consider a simple transfer learning approach exploiting pre-trained convolutional features as input for a fast-to-train kernel method.
Efficient Unsupervised Learning for Plankton Images
We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images.
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