Image classification is a fundamental application in computer vision. Recently, deeper
networks and highly connected networks have shown state of the art performance for image …

Abstract Developing Deep Convolutional Neural Networks (DCNNs) for image classification
is a complicated task that needs considerable effort and knowledge. By employing an …

The principal component analysis network (PCANet), which is one of the recently proposed
deep learning architectures, achieves the state-of-the-art classification accuracy in various …

Abstract The Convolutional Neural Networks (CNNs), in domains like computer vision,
mostly reduced the need for handcrafted features due to its ability to learn the problem …

Recent network research has demonstrated that the performance of convolutional neural
networks can be improved by introducing a learning block that captures spatial correlations …

In this work we propose a novel framework named DualNet aiming at learning more
accurate representation for image recognition. Here two parallel neural networks are …

The accuracy of deep convolutional neural networks (CNNs) generally improves when
fueled with high resolution images. However, this often comes at a high computational cost …

Recently, many researches have demonstrated that the attention mechanism has great
potential in improving the performance of deep convolutional neural networks (CNNs) …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

We present a tree-structured network architecture for large-scale image classification. The
trunk of the network contains convolutional layers optimized over all classes. At a given …