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UNIFICATION OF MODELS HAVING RESPECTIVE TARGET CLASSES WITH DISTILLATION

机译:具有特定目标类别并带有蒸馏的模型的统一

摘要

A computer-implemented method for generating unified soft labels is disclosed. In the computer-implemented method, a collection of samples is prepared. Then, a plurality of predictions generated by a plurality of individual trained models is obtained for each sample, in which each individual trained model has an individual class set to form at least partially a unified class set that includes a plurality of target classes. The unified soft labels are estimated for each sample over the target classes in the unified class set from the plurality of the predictions using a relation connecting a first output of each Individual trained model and a second output of the unified model. The unified soft labels are output to train a unified model having the unified class set.
机译:公开了一种用于产生统一的软标签的计算机实现的方法。在计算机实施的方法中,准备了样品的集合。然后,针对每个样本获得由多个单独的训练模型生成的多个预测,其中每个单独的训练模型具有单独的类别集合以至少部分地形成包括多个目标类别的统一类别集合。使用连接每个个体训练模型的第一输出和统一模型的第二输出的关系,从多个预测为统一类别集中的目标类别上的每个样本估计统一软标签。输出统一的软标签以训练具有统一的类集的统一模型。

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