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SYSTEMS AND METHODS FOR TRAINING NEURAL NETWORKS FOR REGRESSION WITHOUT GROUND TRUTH TRAINING SAMPLES
SYSTEMS AND METHODS FOR TRAINING NEURAL NETWORKS FOR REGRESSION WITHOUT GROUND TRUTH TRAINING SAMPLES
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机译:在没有地面真实训练样本的情况下训练神经网络进行回归的系统和方法
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摘要
A method, computer readable medium, and system are disclosed for training a neural network. The method includes the steps of selecting an input sample from a set of training data that includes input samples and noisy target samples, where the input samples and the noisy target samples each correspond to a latent, clean target sample. The input sample is processed by a neural network model to produce an output and a noisy target sample is selected from the set of training data, where the noisy target samples have a distribution relative to the latent, clean target sample. The method also includes adjusting parameter values of the neural network model to reduce differences between the output and the noisy target sample.
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