Large-Scale Few-Shot Classification with Semi-supervised Hierarchical k-Probabilistic PCAs

This paper introduces a hierarchical semi-supervised framework for few-shot classification on large-scale datasets. The method leverages the development of self-learning-based representation learning and proposes a hierarchical semi-supervised classifier called Hierarchical k-Probabilistic Principal Component Analyzers (Hk-PPCAs), on a pretrained generic self-learned feature extractor. The classifier models the feature space using a two-level hierarchical structure. The first-level image classes and second-level super-classes are modeled as Probabilistic Principal Component Analyzers (PPCA) Gaussian distributions, which makes the framework scalable for adding new classes without retraining the whole model. The proposed PPCA-based Gaussian prototypes ensure the stability of classification under the few-shot setting and the hierarchical structure reduces the classification time from O(K) to $O\left( {\sqrt K } \right)$ for K image classes. This makes the approach computationally efficient and practicable in large-scale classification. Experiments on ImageNet-1k and ImageNet-10k show the effectiveness of the proposed approach.