DECONSTRUCTING BINARY CLASSIFIERS IN COMPUTER VISION October 27, 2020 – Posted in: Uncategorized

This paper develops the novel notion of deconstructive learning and proposes a practical model for deconstructing a broad class of binary classifiers commonly used in vision applications. Specifically, the problem studied in this paper is: Given an image-based binary classifier CC as a black-box oracle, how much can we learn of its internal working by simply querying it? In particular, we demonstrate that it is possible to ascertain the type of kernel function used by the classifier and the number of support vectors using only image queries and ascertain the unknown feature space too.

MOHSEN ALI, JEFFREY HO

ASIAN CONFERENCE ON COMPUTER VISION (ACCV) 2014

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