  Deep learning based models have excelled in many computer vision tasks and
appear to surpass humans' performance. However, these models require an
avalanche of expensive human labeled training data and many iterations to train
their large number of parameters. This severely limits their scalability to the
real-world long-tail distributed categories, some of which are with a large
number of instances, but with only a few manually annotated. Learning from such
extremely limited labeled examples is known as Few-shot learning (FSL).
Different to prior arts that leverage meta-learning or data augmentation
strategies to alleviate this extremely data-scarce problem, this paper presents
a statistical approach, dubbed Instance Credibility Inference (ICI) to exploit
the support of unlabeled instances for few-shot visual recognition. Typically,
we repurpose the self-taught learning paradigm to predict pseudo-labels of
unlabeled instances with an initial classifier trained from the few shot and
then select the most confident ones to augment the training set to re-train the
classifier. This is achieved by constructing a (Generalized) Linear Model
(LM/GLM) with incidental parameters to model the mapping from (un-)labeled
features to their (pseudo-)labels, in which the sparsity of the incidental
parameters indicates the credibility of the corresponding pseudo-labeled
instance. We rank the credibility of pseudo-labeled instances along the
regularization path of their corresponding incidental parameters, and the most
trustworthy pseudo-labeled examples are preserved as the augmented labeled
instances. Theoretically, under mild conditions of restricted eigenvalue,
irrepresentability, and large error, our approach is guaranteed to collect all
the correctly-predicted instances from the noisy pseudo-labeled set.
