InterActive: Inter-Layer Activeness Propagation
This addresses the issue of insufficient descriptive power in low-level neurons for computer vision tasks, though it appears incremental as it builds on existing pre-trained networks.
The paper tackled the problem of low-level deep features lacking spatial context from higher layers by proposing InterActive, a top-down activeness propagation algorithm, which achieved state-of-the-art classification performance on multiple image datasets.
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 high-level context and improving the descriptive power of low-level 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.