Methods & Applications in 2026
Imagine a healthcare startup building an AI system to detect rare diseases. The challenge? There isn’t enough labeled data to train a traditional machine learning model. That’s where few-shot learning (FSL) comes in.
From diagnosing complex medical conditions to enhancing natural language processing, few-shot learning is redefining how AI learns from limited examples.
Explore how few-shot learning works, its main techniques and real-world uses, and consider what its future might bring.
What is few-shot learning (FSL)?
Few-shot learning is a machine learning method that allows models to learn effectively from only a small number of examples instead of relying on large, labeled datasets. Unlike traditional supervised learning, which requires many samples for each task, few-shot learning helps models generalize to new situations with minimal training data.
Few-shot learning is part of a broader family of techniques known as shot learning, which includes:
- Few-shot learning: Learns from a few labeled examples.
- One-shot learning: Learns from just one example.
- Zero-shot learning: Makes predictions without any labeled data by using prior knowledge.
These approaches often build on meta-learning and transfer learning methods, enabling models to apply what they’ve already learned to new but related tasks.
Techniques like prototypical networks and embedding-based representations further improve how models process and understand unstructured data in areas such as image recognition, natural language processing, and medical image analysis.
The role of prompt engineering in few-shot learning
Prompt engineering plays a vital role in few-shot learning. In this approach, models receive prompts that include a few examples (few-shot) or none at all (zero-shot) to guide their response generation. This method works well for large language models, which can adapt to new tasks with well-crafted prompts rather than relying on large training datasets or additional fine-tuning.
The main goal of few-shot learning is to help models perform accurately on new tasks without needing task-specific data. By combining effective prompts with minimal examples, few-shot learning allows AI systems to adapt quickly and handle new situations more efficiently.
Learning through Q learning in few-shot scenarios
Although Q-learning is primarily used in reinforcement learning settings, its principles can be extended to few-shot learning tasks that require decision-making.
For instance, when a model must learn optimal actions with limited feedback, Q learning mechanisms help update action-value estimates.
In environments such as robotics or sequential decision-making, this integration enables models to learn through exploration, even with scarce labeled data.
Leveraging distributed representations for generalization
Distributed representations play a central role in helping few-shot learning models generalize across different tasks. By mapping input data into high-dimensional vector spaces, models can recognize and compare semantic relationships between classes.
The representations learned during pre-training provide a foundation for identifying new examples through metric-based methods such as prototypical networks and matching networks. These techniques enable models to measure sample similarity and make accurate predictions, even with limited training data.
The role of distributed training in few-shot learning
Distributed training becomes essential for accelerating experimentation and optimization as few-shot learning models scale in complexity.
Training across multiple computational nodes enables parallel processing of diverse tasks and classes, thereby improving convergence rates.
Distributed training is beneficial when employing meta-learning strategies that require frequent updates across many small training episodes.
How does few-shot learning work?
1. Support set creation
- A small labeled dataset (e.g., a few-shot prompt) is provided for training.
- This dataset includes examples provided for each class.
2. The query set consists of new data
The model receives a query set (unseen data samples) and must correctly classify them.
3. Meta-Learning and Transfer Learning
- Meta learning teaches models how to learn from a few examples by adapting to related tasks.
- Transfer learning helps leverage pre-existing knowledge to recognize new classes without requiring additional training.
4. Embedding space representation
- The model maps input data into an embedding space where similar classes are clustered together.
- Techniques like prototypical networks improve classification by comparing distances in this space.
5. Fine-tuning on the target task
The model is adjusted with additional context to better predict outcomes in the test set.
N-Way K-Shot classification in few-shot learning
Few-shot learning often uses an N-way K-shot framework to train and test models efficiently with limited data.
- N is the number of classes the model needs to recognize.
- K is the number of labeled examples (shots) provided for each class during training.
How N-Way K-Shot classification works
- Support and query Sets: The support set includes K labeled examples for each of the N classes, helping the model learn class representations. The query set contains unlabeled samples that the model must classify based on what it learned from the support set.
- Learning scenarios
- 3-way 2-shot learning: The model learns from 3 classes, each with two examples.
- One-shot learning (K=1): The model is trained on only one instance per class.
- Zero-shot learning (K=0): The model predicts without labeled data, using prior knowledge.
- Training and optimization: The model trains through multiple episodes, each with a different combination of classes and samples. A loss function measures how well the model classifies the query examples, and parameters are updated to reduce that loss over time.
- Generalization to new classes: Unlike traditional learning, meta-learning emphasizes generalization. The model is trained on varying classes in each episode and later tested on entirely new classes it hasn’t seen before. Its effectiveness is judged by how accurately it classifies these unseen examples based on what it has previously learned.
Figure 1: A 3-way 2-shot classification task example in few-shot learning.
Why is it important?
Test base for learning like a human
Humans can spot the difference between handwritten characters after seeing a few examples. However, computers need large amounts of data to classify what they “see” and spot the difference between handwritten characters. Few-shot learning is a test-based approach in which computers are expected to learn from a few examples, as humans do.
Learning for rare cases
By using few-shot learning, machines can learn rare cases. For example, when classifying images of animals, a machine learning model trained with few-shot learning techniques can correctly classify an image of a rare species after being exposed to only a small amount of prior information.
Reducing data collection effort and computational costs
Because few-shot learning requires less data to train a model, the high costs of data collection and labeling are eliminated. A low amount of training data means low dimensionality in the training dataset, which can significantly reduce the computational costs.
What are the applications of few-shot learning?
Few-shot learning is particularly valuable in scenarios where data collection is challenging or costly. Below are key applications of few-shot learning across various domains:
Computer vision
Few-shot learning addresses an important challenge of computer vision: limited labeled images. It helps facilitate rapid model adaptation in areas such as:
- Image classification & object recognition: Enables models to identify and categorize objects with minimal labeled examples, which is crucial for recognizing rare or new classes.
- Facial recognition: Helps identify individuals from a few facial images, enhancing security systems and personalized user experiences.
- Medical imaging: Supports the detection and diagnosis of diseases from limited medical images, aiding in early intervention and treatment planning.
- Autonomous vehicles: Allow self-driving cars to recognize and respond to uncommon obstacles or scenarios by learning from a few instances.
Natural Language Processing (NLP)
In NLP, few-shot learning empowers models to perform language-related tasks with limited textual data:
- Text classification enables categorizing text into predefined labels, such as spam detection or topic identification, with minimal labeled examples.
- Sentiment analysis assesses the sentiment or emotional tone of text, helping businesses understand customer opinions from a few reviews.
- Machine translation facilitates translation between languages with scarce parallel corpora, expanding the reach of information across linguistic barriers.
- User intent classification enhances chatbots’ and virtual assistants’ ability to accurately interpret user intent from limited interactions, thereby improving response relevance.
Audio processing
Data that contains information regarding voices/sounds can be analyzed by acoustic signal processing, and few-shot learning can enable deployment of tasks such as:
- Voice cloning from a few audio samples of the user (e.g., voices in GPS/navigation apps, Alexa, Siri, and more).
- Voice conversion from one user to another.
- Voice conversion across different languages.
Robotics
Few-shot learning enables robots to acquire new skills and adapt to tasks with minimal demonstrations:
- Imitation learning: Robots can replicate complex movements or tasks by observing a single demonstration, reducing the need for extensive programming.
- Manipulation actions allow robots to learn to handle various objects with few examples, enhancing their utility in dynamic environments.
- Visual navigation enables robots to navigate new spaces by learning from limited visual cues, which are essential for exploration and mapping.
Healthcare
In the healthcare industry, few-shot learning contributes to advancements such as:
- Drug discovery: Accelerates the identification of potential drug candidates by learning from a small number of known compounds.
Internet of Things (IoT) analytics
Facilitates the analysis of data from diverse IoT devices with limited labeled data, improving predictive maintenance and anomaly detection.
Mathematical applications
Assists in tasks like curve-fitting, where models learn to approximate functions from a few data points, enhancing scientific computations.
Logical reasoning
Enables models to perform deductive reasoning tasks with minimal examples, advancing artificial intelligence and problem-solving.
What are the different approaches to few-shot learning?
There are several approaches to train models with very few labeled examples per class, categorized into three main types: metric-based, optimization-based, and generative approaches. Below are the primary methods within these categories:
1. Metric-based approaches
These methods learn a feature space where similar instances are close together, enabling the model to classify new examples based on distance metrics.
- Siamese networks: Use twin networks with shared weights to compare pairs of images using a similarity metric like cosine similarity or Euclidean distance.
- Matching networks: Utilize an attention mechanism to compare new samples against a support set in an embedding space.
- Prototypical networks: Compute a class prototype (centroid) for each class in the embedding space, and classify new samples based on their proximity to these prototypes.
- Relation networks: Learn a non-linear similarity function between query and support set examples instead of relying on predefined distance metrics.
2. Optimization-based approaches
These methods aim to train models that can quickly adapt to new tasks with minimal updates.
- Model-Agnostic Meta-Learning (MAML): A meta-learning framework that optimizes for initial weights that allow fast adaptation to new tasks with few gradient updates.
- Reptile: A simpler alternative to MAML, which updates the model weights based on multiple tasks to improve generalization.
- Meta-Learner LSTM: Uses an LSTM-based optimizer to learn an efficient parameter-updating strategy for few-shot classification.
3. Generative approaches
These methods generate additional samples or useful features to enhance learning.
- Data augmentation: Uses techniques like GANs (e.g., Conditional GANs, Adversarial Autoencoders) or Variational Autoencoders (VAEs) to generate synthetic data for training.
- Memory-augmented networks: Utilize external memory modules (e.g., Neural Turing Machines) to store and retrieve past experiences to aid classification.
4. Hybrid approaches
- Meta-learning with prototypical networks: Combines meta-learning principles with prototypical networks to enhance adaptability.
- Graph Neural Networks (GNNs) for few-shot learning: Model relationships between support and query samples using graph structures.
How is it implemented in Python?
There are several open-source few-shot learning projects available. To implement few-shot learning projects, users can refer to the following libraries/repositories in Python:
- Pytorch – Torchmeta: A library for both few-shot classification and regression problems, which enables easy benchmarking on multiple problems and reproducibility.
- FewRel: A large-scale few-shot relation extraction dataset, which contains more than one hundred relations and lots of annotated instances across different domains.
- Meta Transfer Learning: This repository contains the TensorFlow and PyTorch implementations for Meta-Transfer Learning for Few-Shot Learning.
- Few-Shot: A Repository that contains clean, readable, and tested code to reproduce few-shot learning research.
- Few-Shot Object Detection (FsDet): Contains the official few-shot object detection implementation of Simple Few-Shot Object Detection.
- Prototypical Networks on the Omniglot Dataset: An implementation of “Prototypical Networks for Few-shot Learning” on a notebook in Pytorch.
Future of few-shot learning
The future of few-shot learning, particularly in open-world settings, is expected to evolve in several key areas:
Handling varying concepts
Traditional models assume each instance represents a single concept, but in open-world scenarios, an instance may embody multiple concepts simultaneously. Approaches like multi-label learning are being explored to address this complexity.
Defending against adversarial attacks
As few-shot models become more widely used, they need to be resistant to adversarial attacks. Future research should focus on developing models that can withstand these disruptions, enhancing their reliability in real-world applications.
Cross-domain and incremental learning
Few-shot learning models are being extended to handle data from different domains and new, unseen classes over time. Techniques like incremental learning will allow models to continue learning as new classes emerge, without forgetting previously learned classes.
Data augmentation and multi-phase learning
Future approaches will likely include better data augmentation strategies, improved generalization across domains, and multi-phase learning to handle continuously evolving data.
Embedding few-shot learning into broader learning systems
Few-shot learning contributes to the development of more adaptive and efficient learning systems.
These learning systems combine different learning paradigms, including supervised, unsupervised, and reinforcement learning, to solve complex real-world problems.
By embedding few-shot learning modules, learning systems can maintain performance in data-scarce settings and extend functionality across domains.
Principal Analyst
Cem Dilmegani
Principal Analyst
Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Sıla Ermut
Industry Analyst
Sıla Ermut is an industry analyst at AIMultiple focused on email marketing and sales videos. She previously worked as a recruiter in project management and consulting firms. Sıla holds a Master of Science degree in Social Psychology and a Bachelor of Arts degree in International Relations.
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