# A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains

arXiv: https://arxiv.org/abs/2504.04017 · HTML: https://mlsanity.com/paper/2504.04017/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 84 of its 214 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Builds on (most cited first, 30 of 84)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions](https://mlsanity.com/paper/1812.03664/) (2018)
- [LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning](https://mlsanity.com/paper/2206.06522/) (2022)
- [SpotTune: Transfer Learning through Adaptive Fine-tuning](https://mlsanity.com/paper/1811.08737/) (2018)
- [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://mlsanity.com/paper/2302.13007/) (2023)
- [Adaptive Cross-Modal Few-Shot Learning](https://mlsanity.com/paper/1902.07104/) (2019)
- [Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models](https://mlsanity.com/paper/2301.06267/) (2023)
- [Few-Shot Bot: Prompt-Based Learning for Dialogue Systems](https://mlsanity.com/paper/2110.08118/) (2021)
- [Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning](https://mlsanity.com/paper/2206.03996/) (2022)
- [Few-Shot Open-Set Recognition using Meta-Learning](https://mlsanity.com/paper/2005.13713/) (2020)
- [A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification](https://mlsanity.com/paper/1910.04176/) (2019)
- [Self-Supervised Learning For Few-Shot Image Classification](https://mlsanity.com/paper/1911.06045/) (2019)
- [UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling](https://mlsanity.com/paper/2302.06605/) (2023)
- [Transfer learning for music classification and regression tasks](https://mlsanity.com/paper/1703.09179/) (2017)
- [GPS: Genetic Prompt Search for Efficient Few-shot Learning](https://mlsanity.com/paper/2210.17041/) (2022)
- [Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning](https://mlsanity.com/paper/2103.07552/) (2021)
- [Few-shot Medical Image Segmentation with Cycle-resemblance Attention](https://mlsanity.com/paper/2212.03967/) (2022)
- [FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning](https://mlsanity.com/paper/2108.06332/) (2021)
- [Few-Shot Representation Learning for Out-Of-Vocabulary Words](https://mlsanity.com/paper/1907.00505/) (2019)
- [Log Parsing with Prompt-based Few-shot Learning](https://mlsanity.com/paper/2302.07435/) (2023)
- [Few-Shot Keyword Spotting in Any Language](https://mlsanity.com/paper/2104.01454/) (2021)
- [Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers](https://mlsanity.com/paper/2103.14453/) (2021)
- [MetaAudio: A Few-Shot Audio Classification Benchmark](https://mlsanity.com/paper/2204.02121/) (2022)
- [Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech](https://mlsanity.com/paper/2111.04040/) (2021)

## Abstract (arXiv)

In a world where new domains are constantly discovered and machine learning (ML) is applied to automate new tasks every day, challenges arise with the number of samples available to train ML models. While the traditional ML training relies heavily on data volume, finding a large dataset with a lot of usable samples is not always easy, and often the process takes time. For instance, when a new human transmissible disease such as COVID-19 breaks out and there is an immediate surge for rapid diagnosis, followed by rapid isolation of infected individuals from healthy ones to contain the spread, there is an immediate need to create tools/automation using machine learning models. At the early stage of an outbreak, it is not only difficult to obtain a lot of samples, but also difficult to understand the details about the disease, to process the data needed to train a traditional ML model. A solution for this can be a few-shot learning approach. This paper presents challenges and opportunities of few-shot approaches that vary across major domains, i.e., audio, image, text, and their combinations, with their strengths and weaknesses. This detailed understanding can help to adopt appropriate approaches applicable to different domains and applications.