In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by
generative adversarial networks (GAN). To compensate for the lack of training samples in …

Although there have been encouraging breakthroughs in supervised learning since the
renaissance of deep learning, the recognition of large-scale object classes remains a …

Zero-shot learning (ZSL) is a challenging task due to the lack of unseen class data during
training. Existing works attempt to establish a mapping between the visual and class spaces …

A well trained and generalized deep neural network (DNN) should be robust to both seen
and unseen classes. However, the performance of most of the existing supervised DNN …

Zero-shot learning (ZSL) is one of the most promising problems where substantial progress
can potentially be achieved through unsupervised learning, due to distributional differences …

Class imbalance issue has been a major problem in mechanical fault detection, which exists
when the number of instances presenting in a class is significantly fewer than that in another …

Zero-shot learning casts light on lacking unseen class data by transferring knowledge from
seen classes via a joint semantic space. However, the distributions of samples from seen …

Abstract Traditional Generative Adversarial Network (GAN) based Generalized Zero Shot
Learning (GZSL) methods usually suffer from a problem that these methods ignore the …

The growth and success of deep learning approaches can be attributed to two major factors:
availability of hardware resources and availability of large number of training samples. For …

Zero-shot learning (ZSL) aims to recognize unknown categories that are unavailable during
training. Recently, generative models have shown the potential to address this challenging …