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STABLE AND EFFICIENT TRAINING OF ADVERSARIAL MODELS BY AN ITERATED UPDATE OPERATION OF SECOND ORDER OR HIGHER
STABLE AND EFFICIENT TRAINING OF ADVERSARIAL MODELS BY AN ITERATED UPDATE OPERATION OF SECOND ORDER OR HIGHER
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机译：通过迭代更新操作的第二阶或更高的迭代更新操作稳定而有效地培训对抗性模型
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摘要
The training of an adversarial model is performed by respective update operations at each of a set of successive time steps to minimize an objective function having a plurality of loss components. The update operation includes at least one intermediate step of using gradients of the loss components for current values of the numerical parameters to generate intermediate values for the numerical parameters. A different set of intermediate values for each of the numerical parameters may be generated in each intermediate step. The update operation further includes generating respective updates to the current values of each of the numerical parameters based on functions of the gradients of at least one of the loss components with respect to the respective numerical parameters. This is done both for the current values of the numerical parameters and for the intermediate values of the numerical parameters.
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