1,725 research outputs found
Deep Convolutional Ranking for Multilabel Image Annotation
Multilabel image annotation is one of the most important challenges in
computer vision with many real-world applications. While existing work usually
use conventional visual features for multilabel annotation, features based on
Deep Neural Networks have shown potential to significantly boost performance.
In this work, we propose to leverage the advantage of such features and analyze
key components that lead to better performances. Specifically, we show that a
significant performance gain could be obtained by combining convolutional
architectures with approximate top- ranking objectives, as thye naturally
fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset
outperforms the conventional visual features by about 10%, obtaining the best
reported performance in the literature
Surrogate regret bounds for generalized classification performance metrics
We consider optimization of generalized performance metrics for binary
classification by means of surrogate losses. We focus on a class of metrics,
which are linear-fractional functions of the false positive and false negative
rates (examples of which include -measure, Jaccard similarity
coefficient, AM measure, and many others). Our analysis concerns the following
two-step procedure. First, a real-valued function is learned by minimizing
a surrogate loss for binary classification on the training sample. It is
assumed that the surrogate loss is a strongly proper composite loss function
(examples of which include logistic loss, squared-error loss, exponential loss,
etc.). Then, given , a threshold is tuned on a separate
validation sample, by direct optimization of the target performance metric. We
show that the regret of the resulting classifier (obtained from thresholding
on ) measured with respect to the target metric is
upperbounded by the regret of measured with respect to the surrogate loss.
We also extend our results to cover multilabel classification and provide
regret bounds for micro- and macro-averaging measures. Our findings are further
analyzed in a computational study on both synthetic and real data sets.Comment: 22 page
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