  Fine-grained image classification has emerged as a significant challenge
because objects in such images have small inter-class visual differences but
with large variations in pose, lighting, and viewpoints, etc. Most existing
work focuses on highly customized feature extraction via deep network
architectures which have been shown to deliver state of the art performance.
Given that images from distinct classes in fine-grained classification share
significant features of interest, we present a new deep network architecture
that explicitly models shared features and removes their effect to achieve
enhanced classification results. Our modeling of shared features is based on a
new group based learning wherein existing classes are divided into groups and
multiple shared feature patterns are discovered (learned). We call this
framework Group based deep Shared Feature Learning (GSFL) and the resulting
learned network as GSFL-Net. Specifically, the proposed GSFL-Net develops a
specially designed autoencoder which is constrained by a newly proposed Feature
Expression Loss to decompose a set of features into their constituent shared
and discriminative components. During inference, only the discriminative
feature component is used to accomplish the classification task. A key benefit
of our specialized autoencoder is that it is versatile and can be combined with
state-of-the-art fine-grained feature extraction models and trained together
with them to improve their performance directly. Experiments on benchmark
datasets show that GSFL-Net can enhance classification accuracy over the state
of the art with a more interpretable architecture.
