Training good models is the foundation for machine learning and AI, and model training often requires large data sets to create models that work well. But sometimes we don't have a lot of data. In fact, for some problems, we may only have a few training examples.
This is where Few-Shot Learning comes into play.
With Few-Shot Learning, you can create powerful models even with little data.
In this article I explain what Few-Shot Learning is, what the most important Few-Shot Learning approaches are, how Few-Shot Learning is applied and much more.
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What is Few-Shot Learning?
Few-Shot Learning is a Family of techniques within artificial intelligence (AI) and machine learning where we train a model to make accurate predictions with just a few examples.
Traditionally, when training machine learning systems, we need large data sets to achieve high model performance. This is especially true for deep neural networks (i.e. deep learning systems), which often require very large data sets in the pre-training phase.
However, there are situations where we need a model to learn a new task but only have a small training data set.
This is where the "Few-Shot" techniques come into play. Under the right circumstances (often using pre-trained models), we can use "few-shot" techniques to bypass the need for very large data sets and train models that perform well on new tasks even when data resources are scarce.
This ability to help us create models with minimal training examples makes Few-Shot techniques extremely valuable in situations where Data are inherently scarce, difficult to obtain or expensive. For example, few-shot techniques are valuable tools for data-poor tasks such as medical imaging for rare diseases, NLP (Natural Language Processing) for rare languages and some types of object recognition tasks. I will discuss the applications of Few-Shot Learning later in this article.
Later in this article, we will also discuss the most common techniques for implementing and/or improving few-shot learning, such as meta-learning and some data augmentation techniques.
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Differences between one-shot learning and few-shot learning
Few-shot learning and one-shot learning are similar and related in that Both families of techniques can be used to train models that can be trained with limited Training data generalise well. However, Few-Shot and One-Shot techniques differ in one important aspect: the number of "shots" we use. Remember that in shot-based learning, the number of "shots" corresponds to the number of training examples per class.
With one-shot learning, we train the model with just one training example per target class. In Few-Shot Learning, on the other hand, we train the model with "a few" training examples per class. In practice, we usually use between 2 and 10 training examples for few-shot learning, but this usually depends on the problem we want to solve.
While both Few-Shot and One-Shot techniques attempt to create models that generalise well under conditions of scarce data, One-Shot tends to be a more challenging scenario, as with One-Shot we only have one "trial" as opposed to the 2 to 10 we might have with Few-Shot training.
Most important approaches and techniques of Few-Shot Learning
Although there are many techniques that we can use for Few-Shot Learning, the specific techniques can be categorised into broader approaches (i.e. families of techniques):
- Transfer Learning
- Meta-Learning
- Data Augmentation
Let's take a look at these different approaches.
Transfer Learning
Transfer Learning is a very useful approach for Few-Shot Learning.
In transfer learning, a model is trained in advance on a large and usually general data set so that the pre-trained model performs well on a series of general tasks. This pre-training step usually enables the pre-trained model to learn features and attributes that are common in the feature space for the trained classes.
In transfer learning, part of the knowledge is then transferred to a new, secondary model, giving the secondary model a "learning head start". This makes it much easier for the secondary model to learn from new examples, even with limited data (as the secondary model essentially borrows knowledge from the first pre-trained model).
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Meta-Learning
Meta-learning is another powerful approach that we can use for Few-Shot Learning.
Meta-learning is often referred to as "learning how to learn", and techniques of this type allow models to quickly adapt to new tasks with little training data. Essentially, meta-learning provides AI systems with tools that help them adapt to new tasks.
Importantly, unlike traditional model training techniques that focus on a single task, meta-learning techniques involve learning on multiple tasks. More specifically, meta-learning helps a model learn a learning strategy across multiple tasks. This in turn enables the model to generalise better to new and previously unknown tasks after the meta-learning process.
Data Augmentation
Data augmentation is another approach that we can use for few-shot learning.
In data augmentation, we extend the limited training dataset by transforming data examples to change them in some way (either by duplicating existing examples and then applying minor transformations or by directly transforming the existing examples).
For example, in an image classification task, you can mirror, crop, resize, zoom, rotate or change the colour of the transformations during data augmentation.
Ultimately, data augmentation, like many other few-shot techniques, is particularly useful in situations where it is expensive or difficult to collect large amounts of data.
Advantages of Few-Shot Learning
Now that we have discussed some approaches for "shot-based" learning we would like to briefly discuss some of the advantages.
Lower data requirements
Few-shot learning techniques significantly reduce the amount of labelled training data we need for new or specific tasks.
Although it is worth noting that few-shot learning often requires pre-trained models (trained on large datasets), once we attempt to train a model on a new task, we can use few-shot techniques with scarce data resources.
As already mentioned several times, shot-based learning is very useful in situations where data is expensive or difficult to obtain.
Faster training
Although data scarcity is often seen as a challenge in shot-based learning, it also has an important advantage.
Since we train the model with fewer examples in Few-Shot Learning, Few-Shot techniques often train faster.
However, the effect of fee-shot techniques on training speed depends on the exact application and the fee-shot technique used, as some fee-shot techniques are more computationally intensive than others.
Quick adaptation to new tasks
Since models with Few-Shot Learning techniques can quickly adapt to new and previously unknown tasks, Few-Shot Learning is useful in situations where new tasks are likely to arise.
Meta-learning and transfer learning are particularly useful for this type of rapid customisation.
Reduced training and storage costs
Since Few-Shot Learning requires only a few training examples, Few-Shot techniques naturally reduce the costs associated with collecting, labelling and storing data examples and usually also reduce the costs of training models.
However, since few-shot techniques are often based on larger pre-trained models, most of the benefits of reduced training and storage costs apply to the phase in which we train a model on a new task-specific dataset.
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Improved model generalisation
Few-shot techniques can be used to train models that can be transferred to new tasks. This means that models that have been trained using fee-shot techniques can also perform well in new, previously unknown tasks.
Applications and examples
There are a variety of applications for few-shot learning techniques, but let's briefly look at three main areas where we can apply this learning approach: medical imaging, natural language processing and object recognition.
Medical imaging
Experts for Machine Learning and AI often use few-shot learning techniques in medical imaging, where we use AI and machine learning to analyse medical images in order to diagnose diseases and other abnormalities.
We often struggle to obtain sufficient training data for medical imaging tasks due to the lack of specialised imaging equipment, the rarity of some diseases and ethical issues surrounding patient data.
Since only a few medical images are available for many tasks, we can use few-shot learning techniques to recognise diseases and abnormalities with just a few labelled images.
By using Few-Shot techniques (and also other techniques such as pre-training etc.) we can in turn create medical imaging models that can be easily transferred to new cases.
Natural language processing
We can also use few-shot techniques for natural language processing tasks such as Text classification, sentiment analysis and translation use.
As with the other applications, few-shot techniques are particularly useful in scenarios where labelled data is scarce. In NLP, few-shot techniques are particularly useful in scenarios such as rare languages, rare dialects, low-resource languages or linguistic tasks where data is limited.
Techniques such as fine-tuning (e.g. transfer learning) and meta-learning are particularly useful for these types of NLP tasks with few examples.
As with other applications, Few-Shot Learning allows models to adapt to new languages or specific dialects with just a few examples. This helps to improve model generalisation, even if we only have a limited corpus of training examples.
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Few-shot learning in object recognition
As in the other application areas, few-shot techniques work best when we have limited training examples, and in the context of the Computer Vision this happens in situations where we need a system that recognises new or rarely seen objects.
Such situations exist in computer vision for specific tasks such as autonomous vehicle vision systems and safety monitoring systems. In these tasks, the model may need to be able to recognise new or unusual objects in real time.
It is worth noting that metric-based few-shot learning approaches such as Prototypical Networks and Siamese Networks are particularly well suited for object recognition tasks.
By learning with few-shot techniques, these computer vision systems can generalise better and identify new object classes that were not present in the original training data. This in turn helps these systems to become more adaptable in dynamic environments.
Summary
In this article, we have explained that Few-Shot Learning is a family of techniques that allow us to create models that perform well on new tasks even when data is scarce.
We have explained the most important approaches to few-shot learning, namely transfer learning, meta-learning and data augmentation. And we have briefly explained some of the applications and benefits of few-shot learning techniques.
Few-shot learning is an important approach to training good models, and if you want to master AI, you need to know what it is and how it works. Hopefully this article has helped you get started.
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