  Aiming at improving performance of visual classification in a cost-effective
manner, this paper proposes an incremental semi-supervised learning paradigm
called Deep Co-Space (DCS). Unlike many conventional semi-supervised learning
methods usually performing within a fixed feature space, our DCS gradually
propagates information from labeled samples to unlabeled ones along with deep
feature learning. We regard deep feature learning as a series of steps pursuing
feature transformation, i.e., projecting the samples from a previous space into
a new one, which tends to select the reliable unlabeled samples with respect to
this setting. Specifically, for each unlabeled image instance, we measure its
reliability by calculating the category variations of feature transformation
from two different neighborhood variation perspectives, and merged them into an
unified sample mining criterion deriving from Hellinger distance. Then, those
samples keeping stable correlation to their neighboring samples (i.e., having
small category variation in distribution) across the successive feature space
transformation, are automatically received labels and incorporated into the
model for incrementally training in terms of classification. Our extensive
experiments on standard image classification benchmarks (e.g., Caltech-256 and
SUN-397) demonstrate that the proposed framework is capable of effectively
mining from large-scale unlabeled images, which boosts image classification
performance and achieves promising results compared to other semi-supervised
learning methods.
