计算机科学 ›› 2022, Vol. 49 ›› Issue (10): 111-117.doi: 10.11896/jsjkx.210800038
程恺1, 刘满1,2, 王之腾1, 毛绍臣1, 申秋慧1, 张宏军1
CHEN Kai1, LIU Man1,2, WANG Zhi-teng1, MAO Shao-chen1, SHEN Qiu-hui1, ZHANG Hong-jun1
摘要: 注意力神经过程(Attentive Neural Process,ANP)模型采用生成模型的方法,以样本的任意局部上下文点为输入,输出整个样本的分布函数,从而模仿高斯过程回归完成数据补全任务。样本的属性信息可以为样本的生成提供重要信息,然而ANP模型忽略了对属性信息的使用。受条件变分自动编码机(CVAE)模型以标签为条件控制样本生成的启发,文中提出了全局属性注意力神经过程(Global-attribute Attentive Neural Process,GANP),将样本属性嵌入到编码器网络,从而使浅层变量隐含样本属性信息。同时,在解码器网络中加入样本属性作为特征,使模型的生成样本更为准确,特别是当输入上下文点数量稀少时,属性信息能够帮助模型生成更清晰、准确的样本。最后,从定性和定量两个方面证明了GANP性能的优越性,可以看出该模型扩展了NP家族模型的应用范围,从而更灵活、快速、准确地解决只有部分上下文信息时整个样本的数据补全问题。
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