Supplementary Document: Robust and Discriminative Self-Taught Learning
نویسندگان
چکیده
http://research.microsoft.com/enus/projects/objectclassrecognition http://www-nlpir.nist.gov/projects/trecvid/ http://lms.comp.nus.edu.sg/research/NUSWIDE.htm For the auxiliary image data set and the three target data sets described above, following (Gehler & Nowozin, 2009), we extract SIFT descriptors for the experimental images, which are computed on a regular grid on the image with a spacing of 10 pixels and for the four different radii r = 4, 8, 12, 16. The descriptors are subsequently quantized into a vocabulary of 300 visual words that is generated by k-means clustering.
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Robust and Discriminative Self-Taught Learning
The lack of training data is a common challenge in many machine learning problems, which is often tackled by semi-supervised learning methods or transfer learning methods. The former requires unlabeled images from the same distribution as the labeled ones and the latter leverages labeled images from related homogenous tasks. However, these restrictions often cannot be satisfied. To address this...
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