Marco Castelluccio

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We explore the use of convolutional neural networks for the semantic classification of remote sensing scenes. Two recently proposed architectures, CaffeNet and GoogLeNet, are adopted, with three different learning modalities. Besides conventional training from scratch, we resort to pre-trained networks that are only fine-tuned on the target data, so as to(More)
In rapid release development processes, patches that fix critical issues, or implement high-value features are often promoted directly from the development channel to a stabilization channel, potentially skipping one or more stabilization channels. This practice is called patch uplift. Patch uplift is risky, because patches that are rushed through the(More)
We devised an algorithm, inspired by contrast-set mining algorithms such as STUCCO, to automatically find statistically significant properties (correlations) in crash groups. Many earlier works focused on improving the clustering of crashes but, to the best of our knowledge, the problem of automatically describing properties of a cluster of crashes is so(More)
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