What is Few-Shot Learning?

A branch of machine learning that deals with learning from very few examples, by leveraging prior knowledge or meta-learning techniques

Few-shot learning is a branch of machine learning that deals with the challenge of learning from very few examples, typically less than 10 per class¹. This is a realistic scenario in many domains, such as natural language processing, computer vision, and robotics, where obtaining a large amount of labeled data can be difficult, expensive, or time-consuming. Few-shot learning aims to enable machine learning models to generalize well to new, unseen tasks based on a small number of samples given during the training process.

How does few-shot learning work?

Few-shot learning is an example of meta-learning, which is the study of how to learn how to learn. Meta-learning algorithms learn from a set of related tasks, called the meta-training set, and use this knowledge to adapt quickly to new tasks, called the meta-test set. The meta-training set consists of a large number of tasks, each of which has a small number of labeled examples. The meta-test set consists of tasks that are similar but not identical to the ones in the meta-training set. The goal of meta-learning is to learn a model that can perform well on the meta-test set with minimal adaptation.

There are different approaches to meta-learning, such as:

Model-based methods: These methods learn a model that can capture the common features or patterns across different tasks and use them to initialize or update the model for new tasks. For example, MAML (Model-Agnostic Meta-Learning) learns an initial set of parameters that can be fine-tuned with a few gradient steps for any new task.
Metric-based methods: These methods learn a metric or a similarity function that can measure how similar two examples are and use it to compare new examples with the existing ones. For example, [Prototypical Networks] learn a prototype representation for each class and classify new examples based on their distance to the prototypes.
Optimization-based methods: These methods learn an optimization algorithm or a learning strategy that can efficiently update the model parameters for new tasks. For example, [Meta-SGD] learns a per-parameter learning rate that can speed up the convergence for new tasks.

Why is few-shot learning important?

Few-shot learning is important because it can enable the development of more efficient and robust machine learning systems that can handle real-world scenarios where data is scarce or dynamic. Some of the benefits of few-shot learning are:

Reducing the cost and effort of data collection and annotation: Few-shot learning can significantly reduce the amount of data required to train machine learning models, which can save time and money. For example, few-shot learning can be used to train natural language understanding models for new domains or languages with only a few examples per intent or entity.
Improving the flexibility and adaptability of machine learning systems: Few-shot learning can enable machine learning models to learn how to learn and adapt quickly to new tasks or environments based on a few examples. This can make the models more flexible and adaptable to changing situations. For example, few-shot learning can be used to train computer vision models for new object recognition or detection tasks with only a few images per category.
Enhancing the generalization and robustness of machine learning systems: Few-shot learning can improve the generalization and robustness of machine learning models by preventing overfitting and encouraging diversity. By training on a large number of diverse tasks with a small number of examples each, few-shot learning can expose the models to various scenarios and challenges and make them more resilient to noise or outliers. For example, few-shot learning can be used to train robotics models for new manipulation or navigation tasks with only a few demonstrations per skill.

What are some applications of few-shot learning?

Few-shot learning has many potential applications in various domains, such as:

Natural language processing: Few-shot learning can be used to train natural language processing models for tasks such as text classification, sentiment analysis, question answering, natural language generation, etc., with only a few examples per label or query. For example, [GPT-3] is a large-scale language model that can perform various natural language processing tasks in a few-shot or zero-shot manner by using natural language prompts as inputs.
Computer vision: Few-shot learning can be used to train computer vision models for tasks such as image classification, object detection, face recognition, image generation, etc., with only a few images per category or style. For example, [StyleGAN2] is a generative adversarial network that can generate realistic images of faces or other objects with only a few images as references.
Robotics: Few-shot learning can be used to train robotics models for tasks such as manipulation, navigation, coordination, etc., with only a few demonstrations or feedbacks per skill or goal. For example, [PEARL] is a reinforcement learning algorithm that can learn policies for various robotic tasks with only a few trials per task.

Conclusion

Few-shot learning is a branch of machine learning that deals with learning from very few examples, by leveraging prior knowledge or meta-learning techniques. Few-shot learning can enable machine learning models to generalize well to new, unseen tasks based on a small number of samples given during the training process. Few-shot learning can reduce the cost and effort of data collection and annotation, improve the flexibility and adaptability of machine learning systems, and enhance the generalization and robustness of machine learning systems. Few-shot learning has many potential applications in domains such as natural language processing, computer vision, and robotics.

¹: What Is Few Shot Learning? (Definition, Applications) | Built In ²: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks : Prototypical Networks for Few-shot Learning : Meta-SGD: Learning to Learn Quickly for Few-Shot Learning : Language Models are Few-Shot Learners : Analyzing and Improving the Image Quality of StyleGAN : Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

What is Few-Shot Learning?