GDC- Generalized Distribution Calibration for Few-Shot Learning

Understand

Few shot learning is an important problem in machine learning as large labelled datasets take considerable time and effort to assemble.

• Most few-shot learning algorithms suffer from one of two limitations- they either require the design of sophisticated models and loss functions, thus hampering interpretability; or employ statistical techniques but make assumptions that may not hold across different datasets or features.
• Developing on recent work in extrapolating distributions of small sample classes from the most similar larger classes, we propose a Generalized sampling method that learns to estimate few-shot distributions for classification as weighted random variables of all large classes.
• We use a form of covariance shrinkage to provide robustness against singular covariances due to overparameterized features or small datasets.