  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 complementary representations. The
corresponding joint training strategy is introduced which ensures the
utilization of discriminator. We validate D-PCN with several CNN models on two
benchmark datasets: CIFAR-100 and ImageNet32x32, 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 and verify our motivation. Additionally, we apply D-PCN
for segmentation on PASCAL VOC 2012 and also find promotion.
