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Regularized multi-label classification from partially labeled training data
Regularized multi-label classification from partially labeled training data
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机译:来自部分标记的训练数据的正规化多标签分类
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摘要
Aspects of the present disclosure relate to machine learning techniques for training a model to identify each of a number of different classes in images, based on training data where each training image may not be labeled in a complete manner with respect to the classes. The disclosed training techniques use a new label value to indicate when a ground truth value is unknown for a particular class, and do not penalize the machine learning network for output predictions that do not match the label value representing unknown ground truth. Some implementations of the training process can be regularized to impose sparsity on predicted classes in order to avoid false positive predictions.
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