How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
release_m3ndupcmu5a3ngqcdj74cmootm
by
Yikai Wang, Li Zhang, Yuan Yao, Yanwei Fu
2020
Abstract
Deep learning based models have excelled in many computer vision task 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. Learning from such extremely
limited labeled examples is known as Few-shot learning. 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 to exploit the support of unlabeled
instances for few-shot visual recognition. Typically, we repurpose the
self-taught learning paradigm. To do so, we construct 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 corresponding pseudo-labeled
instance. We rank the credibility of pseudo-labels of unlabeled instances along
the regularization path of their corresponding incidental parameters, and the
most trustworthy pseudo-labeled examples are preserved as the augmented labeled
instances.This process is repeated until all the unlabeled samples are
iteratively included in the expanded training set. Theoretically, under mild
conditions of restricted eigenvalue, irrepresentability, and large error, our
approach is guaranteed to collect all the correctly-predicted pseudo-labeled
instances from the noisy pseudo-labeled set. Extensive experiments under two
few-shot settings show that our approach can establish new state of the art on
four widely used few-shot visual recognition benchmark datasets including
miniImageNet, tieredImageNet, CIFAR-FS, and CUB.
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