Gene

Pure mathematics

Discriminative model

Metallurgy

Materials science

Robustness (evolution)

Classifier (UML)

Biochemistry

Computer science

Outlier

Linear subspace

Supervised learning

Pattern recognition (psychology)

Mathematics

Overfitting

Mathematical analysis

Artificial intelligence

Geometry

Chemistry

Subspace topology

Shot (pellet)

Artificial neural network

Generalization

Machine learning

Computer vision

Object recognition requires a generalization capability to avoid overfitting, especially when the samples are extremely few. Generalization from limited samples, usually studied under the umbrella of meta-learning, equips learning techniques with the ability to adapt quickly in dynamical environments and proves to be an essential aspect of life long learning. In this paper, we provide a framework for few-shot learning by introducing dynamic classifiers that are constructed from few samples. A subspace method is exploited as the central block of a dynamic classifier. We will empirically show that such modelling leads to robustness against perturbations (e.g., outliers) and yields competitive results on the task of supervised and semi-supervised few-shot classification. We also develop a discriminative form which can boost the accuracy even further. Our code is available at https://github.com/chrysts/dsn_fewshot

Adaptive Subspaces for Few-Shot Learning