  Deep convolutional neural networks (CNNs) have demonstrated remarkable
success in computer vision by supervisedly learning strong visual feature
representations. However, training CNNs relies heavily on the availability of
exhaustive training data annotations, limiting significantly their deployment
and scalability in many application scenarios. In this work, we introduce a
generic unsupervised deep learning approach to training deep models without the
need for any manual label supervision. Specifically, we progressively discover
sample anchored/centred neighbourhoods to reason and learn the underlying class
decision boundaries iteratively and accumulatively. Every single neighbourhood
is specially formulated so that all the member samples can share the same
unseen class labels at high probability for facilitating the extraction of
class discriminative feature representations during training. Experiments on
image classification show the performance advantages of the proposed method
over the state-of-the-art unsupervised learning models on six benchmarks
including both coarse-grained and fine-grained object image categorisation.
