公开(公告)号：US10755199B2

公开(公告)日：2020-08-25

申请号：US15608517

申请日：2017-05-30

Applicant: Adobe Inc.

Inventor： Mausoom Sarkar ,  Balaji Krishnamurthy ,  Abhishek Sinha ,  Aahitagni Mukherjee

IPC: G06N20/00 ,  G06F7/02 ,  G06N3/08 ,  G06N3/04

Abstract: An introspection network is a machine-learned neural network that accelerates training of other neural networks. The introspection network receives a weight history for each of a plurality of weights from a current training step for a target neural network. A weight history includes at least four values for the weight that are obtained during training of the target neural network up to the current step. The introspection network then provides, for each of the plurality of weights, a respective predicted value, based on the weight history. The predicted value for a weight represents a value for the weight in a future training step for the target neural network. Thus, the predicted value represents a jump in the training steps of the target neural network, which reduces the training time of the target neural network. The introspection network then sets each of the plurality of weights to its respective predicted value.