What is few-shot learning?

Few-shot learning is a machine learning framework where an AI model learns to make accurate predictions based on a small number of labeled examples. The model is trained to recognize general patterns and characteristics that can be applied across various tasks. This approach is particularly useful in fields where data is scarce, such as image recognition and speech processing.

What does few-shot learning mean?

Few-shot learning (FSL) is a framework in the field of machine learning, serving as a foundational structure for programming code. It is designed to train AI models to make accurate predictions using only a small amount of training data. While conventional machine learning methods typically require thousands of data points to produce reliable results, few-shot learning focuses on optimizing the learning process with minimal data input.

The primary goal of few-shot learning is to enable effective learning with just a few examples. This approach is especially valuable in situations where collecting large amounts of labeled data is challenging. Often, the costs associated with data collection are prohibitively high, or only a limited number of examples or samples are available. This is particularly relevant in fields like rare diseases, where instances may be scarce, or with unique manuscripts, where only a few examples exist.

Few-shot learning can be classified as a subset of n-shot learning, typically represented by an N-Way-K-Shot categorization system. In this framework, “N” denotes the number of classes, while “K” indicates the number of examples provided for each class. This domain of artificial intelligence also encompasses one-shot learning (which involves one labeled example per class) and zero-shot learning (which uses no labeled examples). One-shot learning is considered a more advanced variant of few-shot learning, while zero-shot learning is treated as a distinct learning challenge.

How does few-shot learning work?

Even if special algorithms and neural networks can successfully tackle numerous few-shot learning tasks, FSL is primarily characterized by the specific learning problem rather than a particular model architecture. As a result, the variety of FSL methods is quite broad, encompassing techniques such as adapting pre-trained models, employing meta-learning strategies, and utilizing generative models. Below, we will explore these individual approaches in more detail.

Transfer learning

Approaches based on transfer learning focus on adapting previously trained models to tackle new tasks. Instead of training a model from scratch, these methods transfer learned features and representations to the new task through fine-tuning. This process helps avoid overfitting, which is a common issue in supervised learning when working with a limited number of labeled examples—particularly in models with a large number of parameters, such as Convolutional Neural Networks.

A common procedure is to configure a classification model by training new data classes with a few examples. More complex few-shot learning methods often involve adapting the network architecture. Transfer learning is particularly effective if there are strong similarities between the original and the new task or if the original training took place in a similar context.

Approach at data level

Few-shot learning at data level revolves around the concept of generating additional training data to address the challenge of limited sample sizes. This approach is especially useful in scenarios where real-world examples are exceedingly rare, such as with newly discovered species. When there are sufficiently diverse samples, additional data similar to these can be generated – often using generative models like Generative Adversarial Networks. It’s also possible to combine data augmentation with other methods such as meta-learning.

Meta-learning

Meta-learning takes a broader and more indirect approach than classic transfer learning and supervised learning, as the model is not only trained for tasks that correspond to its actual purpose. It learns to solve tasks within a specific context in the short term and recognizes cross-task patterns and structures in the long term. This makes it possible to make predictions about the degree of similarity of data points of any class and to use these findings to solve downstream tasks.

Metrics-based meta-learning

Metric-based meta-learning approaches don’t directly model classification boundaries, but continuous values to represent a specific data sample. Inference here is based on learning new features that measure the similarity between the value and those of individual samples and classes. Metric-based FSL algorithms include the following:

• Sampling networks use contrastive learning to solve binary classification problems. This involves checking whether two samples represent a positive (match) or negative pair (no match).
• Matching networks are capable of handling multiple classifications. They employ a neural network to generate embeddings for each sample within the support and query sets. Classification is performed by comparing the samples in the support set with those in the query set.
• Prototype networks calculate a prototype for each class by averaging the features of the provided samples across all classes. Individual data points are categorized based on their proximity to these class-specific prototypes.
• Relational networks (RN) also use an embedding module, but also utilize a relationship module that generates a nonlinear distance function appropriate to the classification problem.

Optimization-based meta-learning

Optimization-based methods in few-shot learning aim to create initial models or hyperparameters for neural networks that can be efficiently adapted to relevant tasks. To achieve this, they enhance the optimization process through meta-optimization, often utilizing gradient descent optimization techniques.

The best-known optimization-based FSL method is model-agnostic meta-learning (MAML). This doesn’t focus on a specific task, but is suitable for all models that learn by gradient descent. However, so-called LSTM networks (LSTM = Long Short-Term Memory) can also be used to train meta-learning models. A special feature of latent embedding optimization (LEO) is that it learns a generative distribution of task-specific model parameters.

What are the most important areas of application for few-shot learning?

Few-shot learning can be used in a wide variety of ways. Ultimately numerous sectors and research areas benefit from learning efficiently despite a small number of examples. The main areas of application include:

• Computer Vision: While many popular few-shot learning (FSL) algorithms were developed for image classification, they are also effective for more complex computer vision tasks, such as object recognition, which requires precise localization of individual image components.
• Robotics: Few-shot learning has the potential to help robots find their way around new environments faster and accomplish new tasks more quickly.
• Language processing: FSL methods, especially transfer learning, help to adapt Large Language Models trained in advance with substantial amounts of data to specific tasks for which contextual understanding is required. These include text classification and sentiment analysis.
• Healthcare: Few-shot learning is particularly suited for the medical field due to its ability to rapidly learn from unknown and rare classes of data, making it ideal for diagnosing rare diseases where obtaining labeled examples is often challenging.
• Banking: Financial institutions use FSL algorithms during fraud detection to identify anomalous patterns or behaviors in financial transactions. This works even if only a few cases of fraud are available as a data set.

Practical challenges in the implementation of few-shot learning

Implementing few-shot learning presents several practical challenges. One major hurdle is the risk of overfitting, as models with limited training examples often over-learn from the existing data, resulting in poor generalization. Additionally, achieving good performance in few-shot learning necessitates careful adaptation and tuning of the models.

The quality of the available data is a critical success factor in few-shot learning. If the limited examples are not representative or contain errors, it can significantly impact model performance. Additionally, selecting appropriate features and methods to augment the dataset is challenging due to the scarcity of data. Moreover, the computational resources and time needed to train optimized few-shot learning models should not be overlooked.