Dynamic Filtering with Large Sampling Field for ConvNets.

ABSTRACT: We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbour regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples. Such multiple samples enlarge the kernels receptive fields significantly without requiring more parameters. While LS-DFN inherits the advantages of DFN [5], namely avoiding feature map blurring by positionwise kernels while keeping translation invariance, it also efficiently alleviates the overfitting issue caused by much more parameters than normal CNNs. Our model is efficient and can be trained end-to-end via standard back-propagation.

SUBMITTER: Wu J 

PROVIDER: S-EPMC7479921 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature