  Metric learning algorithms aim to learn a distance function that brings the
semantically similar data items together and keeps dissimilar ones at a
distance. The traditional Mahalanobis distance learning is equivalent to find a
linear projection. In contrast, Deep Metric Learning (DML) methods are proposed
that automatically extract features from data and learn a non-linear
transformation from input space to a semantically embedding space. Recently,
many DML methods are proposed focused to enhance the discrimination power of
the learned metric by providing novel sampling strategies or loss functions.
This approach is very helpful when both the training and test examples are
coming from the same set of categories. However, it is less effective in many
applications of DML such as image retrieval and person-reidentification. Here,
the DML should learn general semantic concepts from observed classes and employ
them to rank or identify objects from unseen categories. Neglecting the
generalization ability of the learned representation and just emphasizing to
learn a more discriminative embedding on the observed classes may lead to the
overfitting problem. To address this limitation, we propose a framework to
enhance the generalization power of existing DML methods in a Zero-Shot
Learning (ZSL) setting by general yet discriminative representation learning
and employing a class adversarial neural network. To learn a more general
representation, we propose to employ feature maps of intermediate layers in a
deep neural network and enhance their discrimination power through an attention
mechanism. Besides, a class adversarial network is utilized to enforce the deep
model to seek class invariant features for the DML task. We evaluate our work
on widely used machine vision datasets in a ZSL setting.
