  Deep convolutional networks have recently shown excellent performance on
Fine-Grained Vehicle Classification. Based on these existing works, we consider
that the back-probation algorithm does not focus on extracting less
discriminative feature as much as possible, but focus on that the loss function
equals zero. Intuitively, if we can learn less discriminative features, and
these features still could fit the training data well, the generalization
ability of neural network could be improved. Therefore, we propose a new layer
which is placed between fully connected layers and convolutional layers, called
as Chanel Max Pooling. The proposed layer groups the features map first and
then compress each group into a new feature map by computing maximum of pixels
with same positions in the group of feature maps. Meanwhile, the proposed layer
has an advantage that it could help neural network reduce massive parameters.
Experimental results on two fine-grained vehicle datasets, the Stanford
Cars-196 dataset and the Comp Cars dataset, demonstrate that the proposed layer
could improve classification accuracies of deep neural networks on fine-grained
vehicle classification in the situation that a massive of parameters are
reduced. Moreover, it has a competitive performance with the-state-of-art
performance on the two datasets.
