In the field of artificial intelligence, few shot learning is emerging as a revolutionary approach to solving complex problems with little training data. This innovative technique is having a considerable impact on various fields, from classification to natural language understanding. By enabling models to learn efficiently from a limited number of examples, few shot learning is a promising technique for developing more adaptable, higher-performance AI systems.
This article explores in depth the concept of few shot learning, how it works and its main approaches. We will examine how this method is transforming the machine learning landscape, particularly in areas such as natural language processing. In addition, we'll look at the associated fine-tuning techniques and their role in optimizing few shot models. By understanding these key concepts, data professionals and AI enthusiasts alike will be better equipped to take advantage of this up-and-coming technology!
Can't wait to find out more? Follow the guide.
What is few shot learning?
Definition and key concepts
Few shot learning is an innovative approach in artificial intelligence that enables models to learn new concepts or tasks from a very limited number of examples. limited number of examples. The distinguishing feature of this machine learning method is its ability to classify elements according to their similarity, using very little training data.
At the heart of few shot learning is the notion of meta-learning, where the model "learns to learn". This approach enables algorithms to adapt quickly to new scenarios and generalize efficiently from a small number of samples (which must be rigorously prepared, i.e. you can't do without structured datasets!). The very essence of this technique lies in its ability to exploit prior knowledge to rapidly adapt to new situations.
Few-shot learning is part of a broader category called n-shot learning, which also encompasses one-shot learning (using a single labeled example per class) andzero shot learning (requiring no labeled examples). This family of techniques aims to mimic the human ability to learn from very few examples, representing a significant paradigm shift in the field of artificial intelligence.
Differences from traditional supervised learning
A few shot learning differs considerably from traditional supervised learning in several key respects:
1. Data volume
Unlike traditional methods, which require large quantities of labeled training data, few shot learning allows models to generalize efficiently using only a small number of samples.
2. Adaptability
Few-shot models are designed to adapt quickly to new tasks or categories, often with only a few examples to achieve good performance. In contrast, conventional supervised learning typically uses hundreds or thousands of labeled data points over several training cycles.
3. Sampling efficiency
Thanks to meta-learning techniques, few-shot models can generalize from very few examples, making them particularly effective in data-sparse scenarios.
4. Flexibility
Few shot learning offers a more flexible approach to machine learning, capable of tackling a wide range of tasks with a minimum of additional model training.
Advantages of few shot learning
Few shot learning has several significant advantages that make it a very useful technique in various fields of artificial intelligence:
1. Optimizing resources
By reducing the need to collect and label large quantities of data, few shot learning saves time and resources. This is not to say that we should abandon the data-labeling process (we still need to use high-quality, structured, non-generic datasets), but rather to move up a gear: no more crowdsourcing or usingclickworkers"No more crowdsourcing or using clickworkers to build datasets for your AIs. Think of using expert, specialized teams!
2. Adaptability to rare data
This approach is particularly useful in situations where data is scarce, expensive to obtain or constantly changing. This includes fields such as the study of handwriting, rare diseases or recently discovered endangered species.
3. Continuous learning
Few shot approaches are intrinsically better suited to continuous learning scenarios, where models need to integrate new knowledge without forgetting previously learned information.
4. Versatility
few shot learning is remarkably versatile in many areas, from computer vision tasks such as image classification to natural language processing applications.
5. Cost reduction
By minimizing the need for labeled examples, this technique overcomes the obstacles of prohibitive costs and the specific expertise required to annotate data correctly, notably the licensing costs of data annotation platforms (which often charge by the number of users required, often hundreds to build datasets via crowdsourcing). With few-shot learning, only a few annotators are needed!
💡 Few shot learning represents a significant advance in the field of artificial intelligence, offering a solution to the limitations of traditional learning methods. By enabling models to learn efficiently from a limited number of examples, this approach enables more flexible and adaptive applications of machine learning, particularly useful in scenarios where data is scarce or difficult to obtain.
How does few shot learning work?
Few shot learning is an innovative approach that enables artificial intelligence models to learn efficiently from a limited number of examples. This method relies on sophisticated techniques to overcome the challenges associated with insufficient training data. To understand how it works, it is essential to examine its key components and underlying mechanisms.
The N-way K-shot paradigm
At the heart of few shot learning lies the N-way K-shot classification framework. This terminology describes the fundamental structure of a few-shot learning task.
In this paradigm :
- N-way designates the number of classes that the model must distinguish in a given task.
- K-shot indicates the number of examples provided for each class.
For example, in a medical image classification problem, we could have a5-way 3-shot"task, where the model has to identify 5 different types of bone pathologies from just 3 examples of X-ray images for each pathology.
This framework makes it possible to simulate realistic scenarios where labeled data are rare!
Support set and query set
In few shot learning, data is generally organized into two distinct sets:
1. Support assembly
This set contains the few labeled examples (K shots) for each of the N classes. The model uses this set to learn or adapt to the new task.
2. Query set
These are additional examples of the same N classes, which the model must classify correctly. The model's performance on the query set determines how well it learns from the limited examples in the support set.
This structure makes it possible to assess the model's ability to generalize from a small number of examples and apply this knowledge to new, unseen cases.
Meta-learning and rapid adaptation
Meta-learning, often referred to as "learning to learn", is a central concept in few shot learning. It aims to create models capable of learning efficiently on new tasks with little data. The process generally takes place in two phases:
1. Meta-training
The model is exposed to a variety of similar but distinct tasks. It learns to extract general characteristics and to adapt quickly to new situations.
2. Fine-tuning
When confronted with a new task, the model uses its acquired knowledge to adapt quickly with just a few examples.
A popular approach to meta-learning is the Model-Agnostic Meta-Learning (MAML). MAML optimizes initial model weights to enable rapid adaptation to new tasks with few examples and few gradient steps.
Other methods, such as prototypical networks, relationship networks and matching networks, focus on learning effective similarity metrics to compare new examples with learned class prototypes.
few shot learning is often based on transfer learning, where a model is first pre-trained on a large generic dataset, then refined on the specific task with few examples . This approach leverages general knowledge acquired on similar domains to improve performance on the new task.
By combining these techniques, few shot learning enables AI models to adapt quickly to new problems, promising more flexible and efficient applications in data-scarce fields.
Main approaches to few shot learning
Few-shot learning encompasses a variety of methods aimed at enabling models to learn efficiently from a limited number of examples. Although these approaches can use a variety of algorithms and neural network architectures, most are based ontransfer learningor meta-learning, or a combination of the two. Let's take a look at the main approaches used in few shot learning!
Metric-based approaches
Metric-based approaches focus on learning a distance or similarity function distance or similarity function to efficiently compare new examples with the limited labeled data available. These methods are inspired by the K-nearest neighbor principle, but instead of directly predicting classification by modeling the decision boundary between classes, they generate a continuous vector representation for each data sample.
Popular metric-based methods include :
1. Siamese networks
These networks learn to calculate similarity scores between pairs of inputs.
2. Prototypical networks
They calculate class prototypes and rank new examples according to their distance from these prototypes.
These approaches excel particularly in tasks such as classifying images with few examples, by learning to measure similarities in a way that generalizes well to new classes .
Optimization-based approaches
Optimization-based approaches, also known as gradient-based meta-learning, aim to learn initial model parameters or hyperparameters of a neural network that can be efficiently adjusted for relevant tasks. The aim is to optimize the gradient descent process itself, i.e. to meta-optimize the optimization process.
A popular method in this category is the agnostic meta-learning (AMLM). These approaches generally involve a two-level optimization process:
1. Inner loop
Rapid adaptation to a specific task using a few gradient steps.
2. Outer loop
Optimization of initial model parameters for rapid adaptation to a wide range of tasks.
By learning a set of parameters that can be quickly fine-tuned for new tasks, these approaches enable models to quickly adapt to new scenarios with just a few examples.
Model-based approaches
Model-based approaches focus on theaugmentation or generating additional training data to complement the limited examples available. These techniques aim to increase the effective size of the training set, helping models to learn more robust representations from limited data.
Popular methods in this category include :
1. Data enhancement
This technique applies transformations to existing samples to create new synthetic examples.
2. Generative models
These advanced artificial intelligence models are used to generate realistic, artificial examples based on the limited real-world data available.
It's important to note that the effectiveness of these approaches can vary according to the complexity of the task. For example, few-shot prompting, a popular technique, works well for many tasks, but may be insufficient for more complex reasoning problems. In such cases, more advanced techniques such as chain-of-thought (CoT) prompting have been developed to tackle more complex arithmetic, common sense and symbolic reasoning tasks.
These different approaches to few shot learning offer a variety of solutions to the challenge of learning from a limited number of examples. Each method has its own advantages, and can be adapted to a greater or lesser extent depending on the type of task and the data available.
Conclusion
Few shot learning represents a major advance in the field of artificial intelligence. This innovative approach is having a considerable influence on various fields of application, from computer vision to natural language processing. By enabling models to learn efficiently from few examples, this technique opens up new perspectives for developing more powerful AI systems in scenarios where data is scarce or difficult to obtain.
The various approaches to few shot learning, whether metric, optimization or model-based, offer a variety of solutions to the challenge of learning from a limited number of examples. While each method has its own advantages, the choice of approach often depends on the type of task and the data available. As this technology continues to evolve, it promises to transform the way we approach complex machine learning problems, particularly in areas where labeled data is scarce or expensive to obtain!
Of course, this doesn't mean that quality datasets are useless. On the contrary, the possibility of using less data is an opportunity to build small, qualitative datasets at a reasonable cost. If you'd like to find out more, don't hesitate to contact us !
