What Is Few-Shot Learning?

Few-shot learning is a machine learning framework that enables models to classify inputs and make accurate predictions with only a small amount of training data. This learning technique is popular in fields where training data is limited, such as some areas of computer vision, health care, and environmental sciences. Understanding the fundamentals of few-shot learning, along with its benefits and limitations, can help you determine when to apply this approach effectively in professional settings.

What is few-shot learning?

Few-shot learning is a type of “meta-learning,” which teaches models to adapt to new tasks by “learning how to learn.” The goal is for machine learning models to generalize from just a few examples, similar to how humans can apply knowledge to a new situation without needing extensive practice. 

For example, if you wanted to teach a model to identify cats, a traditional learning approach might involve feeding it hundreds of pictures of different cats. The model would then compare new images with the extensive training set, matching features to recognize the new input. With few-shot learning, you would only provide the model with a handful of cat images, such as a picture of a lion and a picture of a house cat. From these few examples, the model learns the general characteristics of cats, such as facial features, tail length, body proportions, and so on. When exposed to a new species of cat, such as a tiger, the model can apply what it learned from the limited examples to recognize the tiger as a type of cat, even without a specific tiger training image.

Types of few-shot learning

You can choose between several approaches to few-shot learning based on the number of examples and training methods used. The most common learning types include:

Zero-shot learning

When using zero-shot learning, your model learns to make predictions for classes it hasn’t been exposed to before. While many machine learning algorithms rely on pre-labeled training data, annotating data samples for every possible class is not always practical. This is particularly important for areas where data may be scarce, such as for rare diseases or newly discovered species. 

One-shot learning

In contrast with zero-shot learning, one-shot learning involves training a machine learning model using one example per class. This is valuable in scenarios where obtaining multiple samples is difficult or impractical, allowing the model to generalize from a single instance. This is common in applications such as facial recognition systems or signature verification, where only one sample is provided to verify someone’s identity. 

Few-shot learning

Few-shot learning provides a few training examples in each class, typically between two to five samples. It works in the same way as one-shot learning, except the model has access to more data when generalizing to new information. Like one-shot learning, few-shot learning is commonly chosen for applications where limited training data is available, such as for speech recognition, identity verification, and security protocols.

Who uses few-shot learning?

Few-shot learning is widely used by professionals in industries where data is limited or where models must quickly adapt to new information. This approach is valuable in fields like computer vision, robotics, natural language processing, and health care. For example, in health care, professionals might use few-shot learning to help models recognize rare diseases or anomalies with limited medical imaging samples. This provides diagnostic support in cases where gathering extensive data is challenging. In natural language processing, you might use few-shot learning to fine-tune chatbots or voice assistants so they can understand new queries or domains without needing retraining. 

Real-life examples of few-shot learning

Few-shot learning has practical applications across fields with limited training data, making it a useful tool for many different professionals to learn. Other applications of few-shot learning you might see include:

• Facial recognition: Identify individuals based on only a few images.

• Medical imaging: Diagnose rare diseases with limited reference data.

• Wildlife conservation: Track and identify rare species.

• Handwriting verification: Confirm signatures or handwriting with limited examples.

• Passport verification: Scan and verify passports at airports with only one image.

• Robotics: Learn new tasks based on a few examples.

• Natural language processing: Understand new languages with limited training.

Advantages of few-shot learning

Because few-shot learning algorithms can learn effectively from a small number of labeled examples, they have advantages in fields with limited data due to subject type, data privacy concerns, or data preparation costs. This reduces the financial and computational power requirements of many machine learning training algorithms, while increasing efficiency and making development more accessible for organizations with limited resources. 

Few-shot learning also allows models to extend beyond training categories to new domains, helping models adapt more easily to new scenarios without requiring additional data collection and training. 

Disadvantages of few-shot learning

Models trained with few-shot learning can struggle with generalization, as learning from only a few examples may lead to lower accuracy, especially if you have nuanced data classes or classification requires multi-step reasoning. Your model needs clear instructions to avoid generalizing on patterns or relationships that aren’t relevant to your intended purpose, which can be difficult with limited training information. 

Choosing the right number of training examples can also be difficult, even when working with small numbers. You might need to experiment with the number of examples you include in your few-shot learning algorithm in order to achieve your desired results and avoid overfitting or underfitting. 

Experiment with different machine learning styles.

As you continue exploring machine learning, experimenting with different machine learning styles can help you determine the best approach for your application. Each style has its own set of benefits and challenges, and you have to balance factors such as resource availability, computational requirements, and performance. In addition to few-shot learning, a few learning styles to explore as you develop your skills include:

• Supervised learning: Using labeled training data so your model maps inputs to specific outputs.

• Unsupervised learning: Using unlabeled data and allowing the algorithm to naturally find patterns or groupings.

• Semi-supervised learning: Using both labeled and unlabeled data to help your algorithm make accurate predictions and uncover novel insights. 

• Reinforcement learning: Using a “trial-and-error” learning process to help your algorithm make decisions.

• Transfer learning: Using pre-trained models to adapt to new, related applications. 

Keep exploring machine learning on Coursera

Few-shot learning is a machine learning approach that uses limited labeled training data, allowing the algorithm to learn to generalize small volumes of information to new classes and inputs. As a beginner, a great way to expand your understanding of different machine learning approaches and algorithms is by completing a Specialization on a learning platform like Coursera. Specializations like the Machine Learning Specialization by Stanford and DeepLearning.AI provide an opportunity for you to explore new and exciting machine learning concepts at your own pace.