We introduce a gated value network (GVN) for general multilabel classification (MLC) tasks. GVN was motivated by deep value network (DVN) that directly exploits the “compatibility” metric as the learning pursuit for MLC. Meanwhile, it further improves traditional DVN on twofold. First, GVN relaxes the complex variable optimization steps in DVN inference by incorporating a feedforward predictor for straightforward multilabel prediction. Second, GVN also introduces the gating mechanism to block confounding factors from the input data that allows more precise compatibility evaluations for data and their potential multilabels. The whole GVN framework is trained in an end-to-end manner with policy gradient approaches. We show the effectiveness and generalization of GVN on diverse learning tasks, including document classification, audio tagging, and image attribute prediction.

中文翻译：

---

用于多标签分类的门控值网络

我们为一般多标签分类 (MLC) 任务引入了门控值网络 (GVN)。GVN 受到深度价值网络 (DVN) 的推动，该网络直接利用“兼容性”度量作为 MLC 的学习追求。同时，它在双重方面进一步改进了传统的DVN。首先，GVN 通过为直接的多标签预测合并前馈预测器，放宽了 DVN 推理中的复杂变量优化步骤。其次，GVN 还引入了门控机制来阻止输入数据中的混杂因素，从而可以对数据及其潜在的多标签进行更精确的兼容性评估。整个 GVN 框架使用策略梯度方法以端到端的方式进行训练。我们展示了 GVN 在各种学习任务上的有效性和泛化性，包括文档分类、音频标记、