# GDC- Generalized Distribution Calibration for Few-Shot Learning

arXiv: https://arxiv.org/abs/2204.05230 · HTML: https://mlsanity.com/paper/2204.05230/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 52 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 27 of 27)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Cross Attention Network for Few-shot Classification](https://mlsanity.com/paper/1910.07677/) (2019)
- [Free Lunch for Few-shot Learning: Distribution Calibration](https://mlsanity.com/paper/2101.06395/) (2021)
- [Charting the Right Manifold: Manifold Mixup for Few-shot Learning](https://mlsanity.com/paper/1907.12087/) (2019)
- [Negative Margin Matters: Understanding Margin in Few-shot Classification](https://mlsanity.com/paper/2003.12060/) (2020)
- [Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning](https://mlsanity.com/paper/1903.12290/) (2019)
- [Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective](https://mlsanity.com/paper/2002.10826/) (2020)
- [Implicit Semantic Data Augmentation for Deep Networks](https://mlsanity.com/paper/1909.12220/) (2019)
- [Finding Task-Relevant Features for Few-Shot Learning by Category Traversal](https://mlsanity.com/paper/1905.11116/) (2019)
- [Prototype Rectification for Few-Shot Learning](https://mlsanity.com/paper/1911.10713/) (2019)
- [Multi-level Semantic Feature Augmentation for One-shot Learning](https://mlsanity.com/paper/1804.05298/) (2018)
- [Prototype Completion with Primitive Knowledge for Few-Shot Learning](https://mlsanity.com/paper/2009.04960/) (2020)
- [An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning](https://mlsanity.com/paper/1904.08479/) (2019)
- [Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/1908.01313/) (2019)
- [Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples](https://mlsanity.com/paper/1805.04288/) (2018)
- [Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation](https://mlsanity.com/paper/1902.09884/) (2019)
- [One-Shot Image Classification by Learning to Restore Prototypes](https://mlsanity.com/paper/2005.01234/) (2020)
- [Multi-level Metric Learning for Few-shot Image Recognition](https://mlsanity.com/paper/2103.11383/) (2021)

## Built on by (most cited first, 5 of 5)

- [FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning](https://mlsanity.com/paper/2309.14062/) (2023)
- [Class-Incremental Learning with CLIP: Adaptive Representation Adjustment and Parameter Fusion](https://mlsanity.com/paper/2407.14143/) (2024)
- [How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentation](https://mlsanity.com/paper/2205.07874/) (2022)
- [Diversity Covariance-Aware Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2503.01531/) (2025)
- [Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images](https://mlsanity.com/paper/2409.03354/) (2024)

## Abstract (arXiv)

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. We show that our sampled points are close to few-shot classes even in cases when there are no similar large classes in the training set. Our method works with arbitrary off-the-shelf feature extractors and outperforms existing state-of-the-art on miniImagenet, CUB and Stanford Dogs datasets by 3% to 5% on 5way-1shot and 5way-5shot tasks and by 1% in challenging cross domain tasks.