  Deep learning has recently achieved remarkable performance in image
classification tasks, which depends heavily on massive annotation. However, the
classification mechanism of existing deep learning models seems to contrast to
humans' recognition mechanism. With only a glance at an image of the object
even unknown type, humans can quickly and precisely find other same category
objects from massive images, which benefits from daily recognition of various
objects. In this paper, we attempt to build a generalizable framework that
emulates the humans' recognition mechanism in the image classification task,
hoping to improve the classification performance on unseen categories with the
support of annotations of other categories. Specifically, we investigate a new
task termed Comparison Knowledge Translation (CKT). Given a set of fully
labeled categories, CKT aims to translate the comparison knowledge learned from
the labeled categories to a set of novel categories. To this end, we put
forward a Comparison Classification Translation Network (CCT-Net), which
comprises a comparison classifier and a matching discriminator. The comparison
classifier is devised to classify whether two images belong to the same
category or not, while the matching discriminator works together in an
adversarial manner to ensure whether classified results match the truth.
Exhaustive experiments show that CCT-Net achieves surprising generalization
ability on unseen categories and SOTA performance on target categories.
