Representation learning systems typically rely on massive amounts of labeled
data in order to be trained to high accuracy. Recently, high-dimensional
parametric models like neural networks have succeeded in building rich
representations using either compressive, reconstructive or supervised
criteria. However, the semantic structure inherent in observations is
oftentimes lost in the process. Human perception excels at understanding
semantics but cannot always be expressed in terms of labels. Thus,
\emph{oracles} or \emph{human-in-the-loop systems}, for example crowdsourcing,
are often employed to generate similarity constraints using an implicit
similarity function encoded in human perception. In this work we propose to
combine \emph{generative unsupervised feature learning} with a
\emph{probabilistic treatment of oracle information like triplets} in order to
transfer implicit privileged oracle knowledge into explicit nonlinear Bayesian
latent factor models of the observations. We use a fast variational algorithm
to learn the joint model and demonstrate applicability to a well-known image
dataset. We show how implicit triplet information can provide rich information
to learn representations that outperform previous metric learning approaches as
well as generative models without this side-information in a variety of
predictive tasks. In addition, we illustrate that the proposed approach
compartmentalizes the latent spaces semantically which allows interpretation of
the latent variables.

提出一种将生成式无监督特征学习与概率上的三元组信息处理相结合的方法，将隐式的 oracle 知识转移为显式的非线性贝叶斯潜在因子模型，并证明该方法在学习表示方面优于以前的度量学习方法和没有此类副信息的生成模型。