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MICROTRAINING FOR ITERATIVE FEW-SHOT REFINEMENT OF A NEURAL NETWORK

机译:神经网络迭代近摄细化的微调

摘要

The disclosed microtraining techniques improve accuracy of trained neural networks by performing iterative refinement at low learning rates using a relatively short series microtraining steps. A neural network training framework receives the trained neural network along with a second training dataset and set of hyperparameters. The neural network training framework produces a microtrained neural network by adjusting one or more weights of the trained neural network using a lower learning rate to facilitate incremental accuracy improvements without substantially altering the computational structure of the trained neural network. The microtrained neural network may be assessed for changes in accuracy and/or quality. Additional microtraining sessions may be performed on the microtrained neural network to further improve accuracy or quality.
机译:所公开的微岭技术通过使用相对短的系列微调步骤以低学习速率执行迭代细化来提高培训的神经网络的精度。 神经网络训练框架接收训练有素的神经网络以及第二次训练数据集和一组超参数。 神经网络训练框架通过使用较低的学习速率调整训练的神经网络的一个或多个重量来产生微型神经网络,以便于增量精度改进而不基本改变训练的神经网络的计算结构。 可以评估微rγ的神经网络以进行准确性和/或质量的变化。 可以在微型神经网络上执行额外的微曝光会话,以进一步提高精度或质量。

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