  Building robust and real-time classifiers with diverse datasets are one of
the most significant challenges to deep learning researchers. It is because
there is a considerable gap between a model built with training (seen) data and
real (unseen) data in applications. Recent works including Zero-Shot Learning
(ZSL), have attempted to deal with this problem of overcoming the apparent gap
through transfer learning. In this paper, we propose a novel model, called
Class Representative Learning Model (CRL), that can be especially effective in
image classification influenced by ZSL. In the CRL model, first, the learning
step is to build class representatives to represent classes in datasets by
aggregating prominent features extracted from a Convolutional Neural Network
(CNN). Second, the inferencing step in CRL is to match between the class
representatives and new data. The proposed CRL model demonstrated superior
performance compared to the current state-of-the-art research in ZSL and mobile
deep learning. The proposed CRL model has been implemented and evaluated in a
parallel environment, using Apache Spark, for both distributed learning and
recognition. An extensive experimental study on the benchmark datasets,
ImageNet-1K, CalTech-101, CalTech-256, CIFAR-100, shows that CRL can build a
class distribution model with drastic improvement in learning and recognition
performance without sacrificing accuracy compared to the state-of-the-art
performances in image classification.
