Graph Structured Prediction Energy Networks
Graph Structured Prediction Energy Networks
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发表时间：
2019-10
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通讯作者：
Colin Graber;A. Schwing
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作者：
Colin Graber;A. Schwing
对于多变量的联合推理，已经开发了各种结构化预测技术来对变量之间的相关性进行建模，从而改善预测。然而，许多经典方法存在两个主要缺陷之一：它们要么缺乏对变量之间的高阶相关性进行建模的能力，同时又保持了易于计算的推理，要么它们不允许显式地对已知的相关性进行建模。为了解决这一缺点，我们引入了“图结构预测能量网络”，我们为其开发了推理技术，允许对显式局部和隐式高阶关联进行建模，同时保持推理的易处理性。我们将所提出的方法应用于自然语言处理和计算机视觉领域的任务，并展示了它的普遍实用性。
For joint inference over multiple variables, a variety of structured prediction techniques have been developed to model correlations among variables and thereby improve predictions. However, many classical approaches suffer from one of two primary drawbacks: they either lack the ability to model high-order correlations among variables while maintaining computationally tractable inference, or they do not allow to explicitly model known correlations. To address this shortcoming, we introduce ‘Graph Structured Prediction Energy Networks,’ for which we develop inference techniques that allow to both model explicit local and implicit higher-order correlations while maintaining tractability of inference. We apply the proposed method to tasks from the natural language processing and computer vision domain and demonstrate its general utility.