InterActive: Inter-layer activeness propagation
- Publication Type:
- Conference Proceeding
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, 2016-January pp. 270 - 279
- Issue Date:
An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network. Despite the astonishing performance, deep features extracted from low-level neurons are still below satisfaction, arguably because they cannot access the spatial context contained in the higher layers. In this paper, we present InterActive, a novel algorithm which computes the activeness of neurons and network connections. Activeness is propagated through a neural network in a top-down manner, carrying highlevel context and improving the descriptive power of lowlevel and mid-level neurons. Visualization indicates that neuron activeness can be interpreted as spatial-weighted neuron responses. We achieve state-of-the-art classification performance on a wide range of image datasets.
Please use this identifier to cite or link to this item: