Zero-Shot Learning Via Recurrent Knowledge Transfer

Abstract

Zero-shot learning (ZSL) which aims to learn new concepts without any labeled training data is a promising solution to large-scale concept learning. Recently, many works implement zero-shot learning by transferring structural knowledge from the semantic embedding space to the image feature space. However, we observe that such direct knowledge transfer may suffer from the space shift problem in the form of the inconsistency of geometric structures in the training and testing spaces. To alleviate this problem, we propose a novel method which actualizes recurrent knowledge transfer (RecKT) between the two spaces. Specifically, we unite the two spaces into the joint embedding space in which unseen image data are missing. The proposed method provides a synthesis-refinement mechanism to learn the shared subspace structure (SSS) and synthesize missing data simultaneously in the joint embedding space. The synthesized unseen image data are utilized to construct the classifier for unseen classes. Experimental results show that our method outperforms the state-of-the-art on three popular datasets. The ablation experiment and visualization of the learning process illustrate how our method can alleviate the space shift problem. By product, our method provides a perspective to interpret the ZSL performance by implementing subspace clustering on the learned SSS.

Transfer Learning to Enhance Amenorrhea Status Prediction in Cancer and Fertility Data with Missing Values

“…In heterogeneous transfer learning (HeteTL) , features from source and target do not share the same feature space(𝒳 " ≠ 𝒳 ! ), a typical case is transferring the info from image to text (Zhao, Sun, Hong, Yao, & Wang, 2019) and it does not require that the inputs should have an identical space and distribution. Formally, homogeneous and heterogeneous transfer learning are defined as follows.…”