Combining Low-Density Separators with CNNs.
NIPS(2016)
摘要
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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