  Deep Convolutional Neural Networks (CNN) have exhibited superior performance
in many visual recognition tasks including image classification, object
detection, and scene label- ing, due to their large learning capacity and
resistance to overfit. For the image classification task, most of the current
deep CNN- based approaches take the whole size-normalized image as input and
have achieved quite promising results. Compared with the previously dominating
approaches based on feature extraction, pooling, and classification, the deep
CNN-based approaches mainly rely on the learning capability of deep CNN to
achieve superior results: the burden of minimizing intra-class variation while
maximizing inter-class difference is entirely dependent on the implicit feature
learning component of deep CNN; we rely upon the implicitly learned filters and
pooling component to select the discriminative regions, which correspond to the
activated neurons. However, if the irrelevant regions constitute a large
portion of the image of interest, the classification performance of the deep
CNN, which takes the whole image as input, can be heavily affected. To solve
this issue, we propose a novel latent CNN framework, which treats the most
discriminate region as a latent variable. We can jointly learn the global CNN
with the latent CNN to avoid the aforementioned big irrelevant region issue,
and our experimental results show the evident advantage of the proposed latent
CNN over traditional deep CNN: latent CNN outperforms the state-of-the-art
performance of deep CNN on standard benchmark datasets including the CIFAR-10,
CIFAR- 100, MNIST and PASCAL VOC 2007 Classification dataset.
