Few Shot Learning: Definition and Use Cases
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 has a significant impact on a variety of fields, from classification to natural language understanding. By allowing models to learn effectively from a limited number of examples, few shot learning is a promising technique for developing more adaptable and efficient AI systems.
This article explores in depth the concept of few shot learning, how it works, and its main approaches. We'll look at how this method is transforming the machine learning landscape, especially in areas like natural language processing. In addition, we will discuss the associated fine-tuning techniques and their role in optimizing few shot models. By understanding these key concepts, data professionals and AI enthusiasts will be better equipped to take advantage of this emerging technology!
Are you eager to find out more? Follow the guide.
What is a few shot apprenticeship?
Definition and key concepts
Few shot learning is an innovative approach in the field of artificial intelligence that allows models to learn new concepts or tasks from a very limited number of examples. This machine learning method is distinguished by its ability to classify items based on 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 allows algorithms to adapt quickly to new scenarios and to generalize effectively from a small number of samples (which must still be prepared rigorously, i.e.: you will not be able to do without structured data sets!). The essence of this technique lies in its ability to exploit previous knowledge to adapt quickly to new situations.
Few-shot learning is part of a larger category called n-shot learning, which also includes one-shot learning (using only one labeled example per class) and zero shot learning (not requiring any labelled examples). This family of techniques aims to mimic the human ability to learn from very few examples, which represents a significant paradigm shift in the field of artificial intelligence.
Differences with traditional supervised learning
Few shot learning differs considerably from traditional supervised learning in several key aspects:
1. Data volume
Unlike traditional methods that require large amounts of labeled training data, few shot training allows models to generalize effectively using only a small number of samples.
2. Adaptability
Few shot templates are designed to adapt quickly to new tasks or categories, often with only a few examples to get good performance. In contrast, conventional supervised learning typically uses hundreds or thousands of labeled data points across multiple training cycles.
3. Sampling efficiency
Thanks to meta-learning techniques, few shot models can generalize from very few examples, making them particularly effective in scenarios where data is scarce.
4. Flexibility
Few shot learning offers a more flexible approach to machine learning that can tackle a wide range of tasks with minimal additional model training.
Benefits of learning a few shots
Few shot learning has several significant advantages that make it a very useful technique in various fields of artificial intelligence:
1. Optimization of resources
By reducing the need to collect and label large amounts of data, few shot learning saves time and resources. This does not mean that we should abandon the Data Labeling process (it is always necessary to use quality and structured datasets, not generic), but rather to move up the range: no more Crowdsourcing or the use of ”Clickworkers“ to create data sets for your AIs. Remember to call on expert and 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 areas such as the study of handwriting, rare diseases, or recently discovered endangered species.
3. Continuous learning
Few shot approaches are inherently better suited to continuous learning scenarios, where models need to incorporate new knowledge without forgetting previously learned information.
4. Versatility
Few shot learning demonstrates remarkable versatility in many areas, ranging 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 makes it possible to overcome obstacles related to prohibitive costs and the specific expertise required to properly annotate data, including the costs associated with licensing data annotation platforms (which often charge for 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 allowing models to learn effectively from a limited number of examples, this approach allows for more flexible and adaptive applications of machine learning, especially 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 allows artificial intelligence models to learn effectively from a limited number of examples. This method uses 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 the few shot learning is the N-way K-shot classification framework. This terminology describes the fundamental structure of a few shot learning task.
In this paradigm:
- N-way refers to the number of classes that the model must distinguish in a given task.
- K-shot shows the number of examples provided for each class.
For example, in a problem of classifying medical images, we could have a task”5-way 3-shot“, where the model must identify 5 different types of bone pathologies from only 3 examples of radiographic images for each pathology.
This framework makes it possible to simulate realistic scenarios where labelled data is rare!
Support package and request set
In fee-shot learning, data is generally organized into two distinct sets:
1. Support set
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. Set of queries
These are additional examples of the same N classes, which the model should classify correctly. The performance of the model across the query set determines the quality of its learning from the limited examples in the support set.
💡 This structure makes it possible to assess the ability of the model to generalize from a small number of examples and to apply this knowledge to new cases not seen.
Meta-learning and rapid adaptation
Meta-learning, often referred to as “learning to learn,” is a central concept in rare shot learning. It aims to create models that can learn effectively 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. He learns to extract general characteristics and to adapt quickly to new situations.
2. Fine adaptation
When confronted with a new task, the model uses its acquired knowledge to adapt quickly with only a few examples.
A popular meta-learning approach is the Model-Agnostic Meta-Learning (MAML). MAML optimizes the initial weights of the model to allow rapid adaptation to new tasks with few examples and few gradient steps.
Other methods, like prototypical networks, relationship networks, and matching networks, focus on learning effective similarity metrics to compare new examples to learned class prototypes.
Few shot learning often relies on transfer learning, where a model is first pre-trained on a large generic data set and then refined on the specific task with few examples. This approach makes it possible to take advantage of the general knowledge acquired on similar areas to improve performance on the new task.
👉 By combining these techniques, few shot learning allows AI models to adapt quickly to new problems, promising more flexible and effective applications in areas where data is scarce.
Key Approaches to Few Shot Learning
Few-shot learning encompasses a variety of methods to enable models to learn effectively from a limited number of examples. While these approaches can use a variety of algorithms and neural network architectures, most rely on Transfer Learning, meta-learning, or a combination of both. Let's look at the main approaches used in learning a few shot!
Metrics-based approaches
Metrics-based approaches focus on learning a distance or a similarity function allowing new examples to be effectively compared to the limited labelled data available. These methods are based on the K-nearest neighbors principle, but instead of directly predicting the classification by modeling the decision boundary between classes, they generate a continuous vector representation for each data sample.
Popular methods based on metrics include:
1. Siamese networks
These networks learn to calculate similarity scores between pairs of inputs.
2. Prototypic networks
They calculate the class prototypes and classify the new examples according to their distance from these prototypes.
💡 These approaches particularly excel at tasks such as classifying images with few examples, learning to measure similarities in a way that generalizes well to new classes.
Optimization-based approaches
Optimization-based approaches, also called gradient-based meta-learning, aim to learn the initial parameters of the model or the hyperparameters of a neural network that can be adjusted effectively 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 agnostic meta-learning (MAML). These approaches generally involve an optimization process at two levels:
1. Inner buckle
Fast adaptation to a specific task using a few gradient steps.
2. Outer buckle
Optimization of the initial parameters of the model to allow rapid adaptation to a variety of tasks.
💡 By learning a set of parameters that can be quickly refined for new tasks, these approaches allow models to adapt quickly to new scenarios with just a few examples.
Model-based approaches
Model-based approaches focus on supplementation or the generation of additional training data to complement the limited examples available. These techniques aim to increase the effective size of the training set, thereby helping models learn more robust representations from limited data.
Among the popular methods in this category are:
1. Increase in data
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 and artificial examples based on the limited real data available.
It is important to note that the effectiveness of these approaches may vary depending on the complexity of the task. For example, the Few Shot Prompting, a popular technique, works well for many tasks, but may not be sufficient for more complex reasoning problems. In these cases, more advanced techniques such as chain-of-thought (CoT) prompting were 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 meet the challenge of learning from a limited number of examples. Each method has its own advantages and can be adapted more or less 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 has a considerable influence on various fields of application, from Computer Vision to natural language processing. By allowing models to learn effectively from few examples, this technique opens up new perspectives for developing more efficient AI systems in scenarios where data is rare or difficult to obtain.
The various approaches to few-shot learning, whether based on metrics, optimization, or models, offer a variety of solutions to meet 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, especially in areas where labeled data is rare or expensive to obtain!
Obviously, this does not mean that quality datasets are useless. On the contrary, the possibility of using less data is an opportunity to build qualitative datasets of modest size, at a reasonable cost. If you want to know more, do not hesitate to contact us !
