# Ranking Distance Calibration for Cross-Domain Few-Shot Learning

arXiv: https://arxiv.org/abs/2112.00260 · HTML: https://mlsanity.com/paper/2112.00260/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 56 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 30 of 32)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [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)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?](https://mlsanity.com/paper/2003.11539/) (2020)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Re-ranking Person Re-identification with k-reciprocal Encoding](https://mlsanity.com/paper/1701.08398/) (2017)
- [Hyperbolic Image Embeddings](https://mlsanity.com/paper/1904.02239/) (2019)
- [Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning](https://mlsanity.com/paper/1805.10002/) (2018)
- [Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation](https://mlsanity.com/paper/2001.08735/) (2020)
- [Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation](https://mlsanity.com/paper/2105.08919/) (2021)
- [Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness](https://mlsanity.com/paper/2010.08001/) (2020)
- [High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification](https://mlsanity.com/paper/2003.08177/) (2020)
- [TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning](https://mlsanity.com/paper/1905.06549/) (2019)
- [Self-training for Few-shot Transfer Across Extreme Task Differences](https://mlsanity.com/paper/2010.07734/) (2020)
- [A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking](https://mlsanity.com/paper/1711.10378/) (2017)
- [A Universal Representation Transformer Layer for Few-Shot Image Classification](https://mlsanity.com/paper/2006.11702/) (2020)
- [Cross-Domain Few-Shot Classification via Adversarial Task Augmentation](https://mlsanity.com/paper/2104.14385/) (2021)
- [Explanation-Guided Training for Cross-Domain Few-Shot Classification](https://mlsanity.com/paper/2007.08790/) (2020)
- [Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network](https://mlsanity.com/paper/1505.07922/) (2015)
- [Few-Shot Learning Through an Information Retrieval Lens](https://mlsanity.com/paper/1707.02610/) (2017)
- [Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder](https://mlsanity.com/paper/2108.05028/) (2021)
- [Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data](https://mlsanity.com/paper/2107.11978/) (2021)
- [Cross-Domain Few-Shot Learning by Representation Fusion](https://mlsanity.com/paper/2010.06498/) (2020)
- [Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification](https://mlsanity.com/paper/2005.08463/) (2020)
- [On the Importance of Distractors for Few-Shot Classification](https://mlsanity.com/paper/2109.09883/) (2021)
- [Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2005.09218/) (2020)

## Built on by (most cited first, 27 of 27)

- [StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2302.09309/) (2023)
- [Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification](https://mlsanity.com/paper/2302.08909/) (2023)
- [Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2403.00567/) (2024)
- [Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning](https://mlsanity.com/paper/2306.10511/) (2023)
- [Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey](https://mlsanity.com/paper/2303.08557/) (2023)
- [Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2403.01966/) (2024)
- [Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2411.01432/) (2024)
- [ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2210.05280/) (2022)
- [Towards Few-Shot Learning in the Open World: A Review and Beyond](https://mlsanity.com/paper/2408.09722/) (2024)
- [Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning](https://mlsanity.com/paper/2411.10070/) (2024)
- [Cross-Domain Few-Shot Classification via Inter-Source Stylization](https://mlsanity.com/paper/2208.08015/) (2022)
- [Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow](https://mlsanity.com/paper/2401.17642/) (2024)
- [Random Registers for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2506.02843/) (2025)
- [A Survey of Deep Visual Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2303.09253/) (2023)
- [Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning](https://mlsanity.com/paper/2506.03110/) (2025)
- [Improving Cross-domain Few-shot Classification with Multilayer Perceptron](https://mlsanity.com/paper/2312.09589/) (2023)
- [Free Lunch to Meet the Gap: Intermediate Domain Reconstruction for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2511.14279/) (2025)
- [Adaptive Semantic Consistency for Cross-domain Few-shot Classification](https://mlsanity.com/paper/2308.00727/) (2023)
- [Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2605.29776/) (2026)
- [Plug-and-Play Feature Generation for Few-Shot Medical Image Classification](https://mlsanity.com/paper/2310.09471/) (2023)
- [Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2312.03928/) (2023)
- [Semantic-Anchored, Class Variance-Optimized Clustering for Robust Semi-Supervised Few-Shot Learning](https://mlsanity.com/paper/2501.14401/) (2025)
- [A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mapping](https://mlsanity.com/paper/2504.06608/) (2025)
- [FreqGRL: Suppressing Low-Frequency Bias and Mining High-Frequency Knowledge for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2511.06648/) (2025)
- [SRasP: Self-Reorientation Adversarial Style Perturbation for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2603.05135/) (2026)
- [When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2608.06673/) (2026)
- [Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions](https://mlsanity.com/paper/2609.10851/) (2026)

## Abstract (arXiv)

Recent progress in few-shot learning promotes a more realistic cross-domain setting, where the source and target datasets are from different domains. Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited. This encourages us to explore more information in the target domain rather than to overly elaborate training strategies on the source domain as in many existing methods. Hence, we start from a generic representation pre-trained by a cross-entropy loss and a conventional distance-based classifier, along with an image retrieval view, to employ a re-ranking process for calibrating a target distance matrix by discovering the reciprocal k-nearest neighbours within the task. Assuming the pre-trained representation is biased towards the source, we construct a non-linear subspace to minimise task-irrelevant features therewithin while keep more transferrable discriminative information by a hyperbolic tangent transformation. The calibrated distance in this target-aware non-linear subspace is complementary to that in the pre-trained representation. To impose such distance calibration information onto the pre-trained representation, a Kullback-Leibler divergence loss is employed to gradually guide the model towards the calibrated distance-based distribution. Extensive evaluations on eight target domains show that this target ranking calibration process can improve conventional distance-based classifiers in few-shot learning.