Random sampling has been proven time and time again to be a powerful tool for working with large data. Queries over the full dataset are replaced by approximate queries over the smaller (and hence easier to store and manipulate) sample. The sample constitutes a flexible summary that supports a...
Thus, when M = 96, GRIT sampling becomes equivalent to random sampling. In Figure 2, we examine the impact of an increasing num- ber of false negatives on the downstream performance of GRIT-VLP. Specifically, we measured the average IRTR score of GRIT-VLP acros...
Generally, sampling negatives is done at random. In this paper, we have improved Negative Bootstrap in an efficient way to obtain most relevant negatives. To obtain most misclassified visually similar images in a faster way, fast intersection kernel SVM is generalized and used for classification....
This gives the probabilities shown in the top half of table 2. We then applied a correction for verification bias, as shown in the appendix. Note that verification status depended solely on the diagnostic test result, therefore fulfilling the missing at random assumption required for this method....
In addition, each image was corrupted independently ten times to reduce the uncertainty of the result caused by the use of a pseudo-random number generator. During the research, we tested different combinations of filtering and demosaicing algorithms; in the final comparison we used the following ...
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