Name
Abstract | ||
|---|---|---|
Recent advances in image classification have been achieved with deep convolutional neural networks (CNNs). The pooling and sub-sampling operations in the CNNs introduce invariance to local transformations, but result in accuracy loss in the image applications. In this paper, we propose a novel deep network called “Weighted Integration Architecture Network” (WIAN) which can effectively recover the information loss due to the pooling operation in the CNNs. The proposed WIAN reuses the information from the previous layers in the network and assigns a weight to each according to the responses or entropy in the layer and then element-wise summing them to further enhance the image classification performance. Exhaustive experiments on four standard benchmark datasets (CIFAR-10, CIFAR-100, MNIST and SVHN) demonstrate the effectiveness as well as an improved performance of WIAN. |
Year | Venue | Field |
|---|---|---|
2016 | PCM | Information loss,MNIST database,Pattern recognition,Invariant (physics),Computer science,Convolutional neural network,Pooling,Artificial intelligence,Contextual image classification,Integration architecture |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Wu | 1 | 90 | 5.58 |
| Michael S. Lew | 2 | 2742 | 166.02 |