# Asymmetric Distribution Measure for Few-shot Learning

arXiv: https://arxiv.org/abs/2002.00153 · HTML: https://mlsanity.com/paper/2002.00153/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2020.

## Built on by (most cited first, 18 of 18)

- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification](https://mlsanity.com/paper/2204.04567/) (2022)
- [LibFewShot: A Comprehensive Library for Few-shot Learning](https://mlsanity.com/paper/2109.04898/) (2021)
- [Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification](https://mlsanity.com/paper/2106.05517/) (2021)
- [Multi-level Metric Learning for Few-shot Image Recognition](https://mlsanity.com/paper/2103.11383/) (2021)
- [Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model](https://mlsanity.com/paper/2405.12081/) (2024)
- [Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding](https://mlsanity.com/paper/2108.02953/) (2021)
- [Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning](https://mlsanity.com/paper/2106.14467/) (2021)
- [Learning Muti-expert Distribution Calibration for Long-tailed Video Classification](https://mlsanity.com/paper/2205.10788/) (2022)
- [Few-shot Classification with Hypersphere Modeling of Prototypes](https://mlsanity.com/paper/2211.05319/) (2022)
- [High-level semantic feature matters few-shot unsupervised domain adaptation](https://mlsanity.com/paper/2301.01956/) (2023)
- [An Adaptive Plug-and-Play Network for Few-Shot Learning](https://mlsanity.com/paper/2302.09326/) (2023)
- [FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?](https://mlsanity.com/paper/2307.04114/) (2023)
- [SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification](https://mlsanity.com/paper/2605.13672/) (2026)
- [Trajectory-aware Principal Manifold Framework for Data Augmentation and Image Generation](https://mlsanity.com/paper/2310.07801/) (2023)
- [Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2604.14958/) (2026)
- [E2MPL:An Enduring and Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation](https://mlsanity.com/paper/2407.04066/) (2024)
- [Task Adaptive Feature Distribution Based Network for Few-shot Fine-grained Target Classification](https://mlsanity.com/paper/2410.09797/) (2024)

## Abstract (arXiv)

The core idea of metric-based few-shot image classification is to directly measure the relations between query images and support classes to learn transferable feature embeddings. Previous work mainly focuses on image-level feature representations, which actually cannot effectively estimate a class's distribution due to the scarcity of samples. Some recent work shows that local descriptor based representations can achieve richer representations than image-level based representations. However, such works are still based on a less effective instance-level metric, especially a symmetric metric, to measure the relations between query images and support classes. Given the natural asymmetric relation between a query image and a support class, we argue that an asymmetric measure is more suitable for metric-based few-shot learning. To that end, we propose a novel Asymmetric Distribution Measure (ADM) network for few-shot learning by calculating a joint local and global asymmetric measure between two multivariate local distributions of queries and classes. Moreover, a task-aware Contrastive Measure Strategy (CMS) is proposed to further enhance the measure function. On popular miniImageNet and tieredImageNet, we achieve 3.02% and 1.56% gains over the state-of-the-art method on the 5-way 1-shot task, respectively, validating our innovative design of asymmetric distribution measures for few-shot learning.