@dinwinchester
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Registered: 4 years ago
Fast, Accurate Metric Learning Many machine learning applications are designed to handle small samples, in order to reduce the variance in the prediction model in the context of a large training set. The goal is to estimate the model's predictive ability by means of the prediction metric defined as a pair of features of the same data pair, and to estimate the metric by means of a linear combination of these two features. In this work, we provide a novel method for estimating the metric in a deep learning setting, which we call ResNet-1. ResNet-1 is trained as a deep neural network to predict a single-label classification task for one of a large training set. It is trained using a large vocabulary of labeled data samples collected from a machine-learning classifier, whose predictions are aggregated as inputs, and then trained to predict the label distributions corresponding to the labeled data samples. Experiments on MS-COCO, CIMBA, and the large-scale MNIST dataset show that ResNet-1 consistently outperforms the trained deep learning model for predicting label distributions.
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