  Using generative models to synthesize visual features from semantic
distribution is one of the most popular solutions to ZSL image classification
in recent years. The triplet loss (TL) is popularly used to generate realistic
visual distributions from semantics by automatically searching discriminative
representations. However, the traditional TL cannot search reliable unseen
disentangled representations due to the unavailability of unseen classes in
ZSL. To alleviate this drawback, we propose in this work a multi-modal triplet
loss (MMTL) which utilizes multimodal information to search a disentangled
representation space. As such, all classes can interplay which can benefit
learning disentangled class representations in the searched space. Furthermore,
we develop a novel model called Disentangling Class Representation Generative
Adversarial Network (DCR-GAN) focusing on exploiting the disentangled
representations in training, feature synthesis, and final recognition stages.
Benefiting from the disentangled representations, DCR-GAN could fit a more
realistic distribution over both seen and unseen features. Extensive
experiments show that our proposed model can lead to superior performance to
the state-of-the-arts on four benchmark datasets. Our code is available at
https://github.com/FouriYe/DCRGAN-TMM.
