Parallel Convolutional Networks for Image Recognition via a Discriminator
This work addresses feature extraction limitations in image recognition for computer vision applications, but it is incremental as it builds on existing CNN architectures.
The paper tackles the problem of enhancing feature extraction in CNNs for image recognition by introducing D-PCN, a framework with parallel CNNs and a discriminator, resulting in state-of-the-art performance on CIFAR-100 and improvements on ImageNet and segmentation tasks.
In this paper, we introduce a simple but quite effective recognition framework dubbed D-PCN, aiming at enhancing feature extracting ability of CNN. The framework consists of two parallel CNNs, a discriminator and an extra classifier which takes integrated features from parallel networks and gives final prediction. The discriminator is core which drives parallel networks to focus on different regions and learn different representations. The corresponding training strategy is introduced to ensures utilization of discriminator. We validate D-PCN with several CNN models on benchmark datasets: CIFAR-100, and ImageNet, D-PCN enhances all models. In particular it yields state of the art performance on CIFAR-100 compared with related works. We also conduct visualization experiment on fine-grained Stanford Dogs dataset to verify our motivation. Additionally, we apply D-PCN for segmentation on PASCAL VOC 2012 and also find promotion.