  Collecting training images for all visual categories is not only expensive
but also impractical. Zero-shot learning (ZSL), especially using attributes,
offers a pragmatic solution to this problem. However, at test time most
attribute-based methods require a full description of attribute associations
for each unseen class. Providing these associations is time consuming and often
requires domain specific knowledge. In this work, we aim to carry out
attribute-based zero-shot classification in an unsupervised manner. We propose
an approach to learn relations that couples class embeddings with their
corresponding attributes. Given only the name of an unseen class, the learned
relationship model is used to automatically predict the class-attribute
associations. Furthermore, our model facilitates transferring attributes across
data sets without additional effort. Integrating knowledge from multiple
sources results in a significant additional improvement in performance. We
evaluate on two public data sets: Animals with Attributes and aPascal/aYahoo.
Our approach outperforms state-of-the-art methods in both predicting
class-attribute associations and unsupervised ZSL by a large margin.
