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Combining Low-Density Separators with CNNs

Yu-Xiong Wang · Martial Hebert

Area 5+6+7+8 #140

Keywords: [ (Application) Object and Pattern Recognition ] [ (Application) Computer Vision ]


This work explores CNNs for the recognition of novel categories from few examples. Inspired by the transferability analysis of CNNs, we introduce an additional unsupervised meta-training stage that exposes multiple top layer units to a large amount of unlabeled real-world images. By encouraging these units to learn diverse sets of low-density separators across the unlabeled data, we capture a more generic, richer description of the visual world, which decouples these units from ties to a specific set of categories. We propose an unsupervised margin maximization that jointly estimates compact high-density regions and infers low-density separators. The low-density separator (LDS) modules can be plugged into any or all of the top layers of a standard CNN architecture. The resulting CNNs, with enhanced generality, significantly improve the performance in scene classification, fine-grained recognition, and action recognition with small training samples.

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