# Low-shot learning with large-scale diffusion

arXiv: https://arxiv.org/abs/1706.02332 · HTML: https://mlsanity.com/paper/1706.02332/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 30 of 33)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification](https://mlsanity.com/paper/2103.16725/) (2021)
- [Learning with Neighbor Consistency for Noisy Labels](https://mlsanity.com/paper/2202.02200/) (2022)
- [Class-Balanced Distillation for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2104.05279/) (2021)
- [Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain](https://mlsanity.com/paper/2108.12296/) (2021)
- [GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph](https://mlsanity.com/paper/2105.11852/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [On the Importance of Firth Bias Reduction in Few-Shot Classification](https://mlsanity.com/paper/2110.02529/) (2021)
- [Approximating Human-Like Few-shot Learning with GPT-based Compression](https://mlsanity.com/paper/2308.06942/) (2023)
- [MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples](https://mlsanity.com/paper/2112.02763/) (2021)
- [Tuning-free Universally-Supervised Semantic Segmentation](https://mlsanity.com/paper/2405.14294/) (2024)
- [Few-Shot Non-Parametric Learning with Deep Latent Variable Model](https://mlsanity.com/paper/2206.11573/) (2022)
- [Local Propagation for Few-Shot Learning](https://mlsanity.com/paper/2101.01480/) (2021)
- [Few-shot Partial Multi-view Learning](https://mlsanity.com/paper/2105.02046/) (2021)
- [Progressive Representative Labeling for Deep Semi-Supervised Learning](https://mlsanity.com/paper/2108.06070/) (2021)
- [Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers](https://mlsanity.com/paper/2112.02353/) (2021)
- [Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction](https://mlsanity.com/paper/2103.07641/) (2021)
- [Cold Start Active Learning Strategies in the Context of Imbalanced Classification](https://mlsanity.com/paper/2201.10227/) (2022)
- [Complementing Semi-Supervised Learning with Uncertainty Quantification](https://mlsanity.com/paper/2207.12131/) (2022)
- [An Extensible Multimodal Multi-task Object Dataset with Materials](https://mlsanity.com/paper/2305.14352/) (2023)
- [Text-to-feature diffusion for audio-visual few-shot learning](https://mlsanity.com/paper/2309.03869/) (2023)
- [Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2403.11070/) (2024)
- [FLIER: Few-shot Language Image Models Embedded with Latent Representations](https://mlsanity.com/paper/2410.07648/) (2024)
- [Few-Shot Partial-Label Learning](https://mlsanity.com/paper/2106.00984/) (2021)
- [Few-shot Learning with Global Relatedness Decoupled-Distillation](https://mlsanity.com/paper/2107.05583/) (2021)
- [External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery](https://mlsanity.com/paper/2107.10504/) (2021)
- [ADT-SSL: Adaptive Dual-Threshold for Semi-Supervised Learning](https://mlsanity.com/paper/2205.10571/) (2022)
- [KGTN-ens: Few-Shot Image Classification with Knowledge Graph Ensembles](https://mlsanity.com/paper/2211.03199/) (2022)
- [Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation](https://mlsanity.com/paper/2301.08413/) (2023)
- [Probability-density-aware Semi-supervised Learning](https://mlsanity.com/paper/2412.17547/) (2024)

## Abstract (arXiv)

This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time. This setup is often referred to as low-shot learning, where a standard approach is to re-train the last few layers of a convolutional neural network learned on separate classes for which training examples are abundant. We consider a semi-supervised setting based on a large collection of images to support label propagation. This is possible by leveraging the recent advances on large-scale similarity graph construction. We show that despite its conceptual simplicity, scaling label propagation up to hundred millions of images leads to state of the art accuracy in the low-shot learning regime.