论文标题
域之间的实例通过RBM中的Gibbs采样过程过渡
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM
论文作者
论文摘要
在本文中，我们提出了基于Gibbs抽样的转移学习（TL）的新想法。 Gibbs采样是一种算法，在该算法中，对于概率分布，可能会转移到具有较高可能性的新状态。我们发现，可以使用这种算法来传输域之间的实例。受限的玻尔兹曼机器（RBM）是一种基于能量的模型，非常可行，可以训练以代表数据分布以及执行Gibbs采样。我们使用RBM捕获源域的数据分布并使用它，以将目标实例施加到具有类似于源数据的分布的新数据中。使用通常用于评估TL方法的数据集，我们表明我们的方法可以通过相当大的比例成功增强目标分类。此外，所提出的方法比常见的DA方法具有优势，即在模型训练过程中不需要目标数据。
In this paper, we present a new idea for Transfer Learning (TL) based on Gibbs Sampling. Gibbs sampling is an algorithm in which instances are likely to transfer to a new state with a higher possibility with respect to a probability distribution. We find that such an algorithm can be employed to transfer instances between domains. Restricted Boltzmann Machine (RBM) is an energy based model that is very feasible for being trained to represent a data distribution and also for performing Gibbs sampling. We used RBM to capture data distribution of the source domain and use it in order to cast target instances into new data with a distribution similar to the distribution of source data. Using datasets that are commonly used for evaluation of TL methods, we show that our method can successfully enhance target classification by a considerable ratio. Additionally, the proposed method has the advantage over common DA methods that it needs no target data during the process of training of models.
