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Adapting an Object Detector by Considering the Worst Case: a Conservative Approach

8 years 2 months ago
Adapting an Object Detector by Considering the Worst Case: a Conservative Approach
The performance of an offline-trained classifier can be improved on-site by adapting the classifier towards newly acquired data. However, the adaptation rate is a tuning parameter affecting the performance gain substantially. Poor selection of the adaptation rate may worsen the performance of the original classifier. To solve this problem, we propose a conservative model adaptation method by considering the worst case during the adaptation process. We first construct a random cover of the set of the adaptation data from its partition. For each element in the cover (i.e. a portion of the whole adaptation data set), we define the crossentropy error function in the form of logistic regression. The element in the cover with the maximum cross-entropy error corresponds to the worst case in the adaptation. Therefore we can convert the conservative model adaptation into the classic min-max optimization problem: finding the adaptation parameters that minimize the maximum of the crossent...
Guang Chen, TonyX. Han
Added 08 Apr 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Guang Chen, TonyX. Han
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