  Few-shot learning (FSL) aims to learn models that generalize to novel classes
with limited training samples. Recent works advance FSL towards a scenario
where unlabeled examples are also available and propose semi-supervised FSL
methods. Another line of methods also cares about the performance of base
classes in addition to the novel ones and thus establishes the incremental FSL
scenario. In this paper, we generalize the above two under a more realistic yet
complex setting, named by Semi-Supervised Incremental Few-Shot Learning (S2
I-FSL). To tackle the task, we propose a novel paradigm containing two parts:
(1) a well-designed meta-training algorithm for mitigating ambiguity between
base and novel classes caused by unreliable pseudo labels and (2) a model
adaptation mechanism to learn discriminative features for novel classes while
preserving base knowledge using few labeled and all the unlabeled data.
Extensive experiments on standard FSL, semi-supervised FSL, incremental FSL,
and the firstly built S2 I-FSL benchmarks demonstrate the effectiveness of our
proposed method.
