  Despite the effectiveness of Convolutional Neural Networks (CNNs) for image
classification, our understanding of the relationship between shape of
convolution kernels and learned representations is limited. In this work, we
explore and employ the relationship between shape of kernels which define
Receptive Fields (RFs) in CNNs for learning of feature representations and
image classification. For this purpose, we first propose a feature
visualization method for visualization of pixel-wise classification score maps
of learned features. Motivated by our experimental results, and observations
reported in the literature for modeling of visual systems, we propose a novel
design of shape of kernels for learning of representations in CNNs. In the
experimental results, we achieved a state-of-the-art classification performance
compared to a base CNN model [28] by reducing the number of parameters and
computational time of the model using the ILSVRC-2012 dataset [24]. The
proposed models also outperform the state-of-the-art models employed on the
CIFAR-10/100 datasets [12] for image classification. Additionally, we analyzed
the robustness of the proposed method to occlusion for classification of
partially occluded images compared with the state-of-the-art methods. Our
results indicate the effectiveness of the proposed approach. The code is
available in github.com/minogame/caffe-qhconv.
