A review of few-shot learning: techniques, algorithms and models

Machine learning has made great progress recently, but there is still a major challenge: the need for large amounts of labeled data to train models.

Sometimes this data is not available in the real world. Take healthcare as an example, we may not have enough X-ray scans to check for a new disease. But through few-shot learning, the model can learn knowledge from only a few examples!

So few-shot learning (FSL) is a subfield of machine learning, which solves the task of learning new tasks with only a small number of labeled examples The problem. The whole point of FSL is to enable machine learning models to learn new things with a little bit of data, which is useful in situations where collecting a bunch of labeled data is too expensive, takes too long, or is impractical.

Few-shot learning method

Support sample/query set: Use a small number of images to classify the query set.

There are three main methods to understand in few-shot learning: meta-learning, data-level and parameter-level.

• Meta-learning: Meta-learning involves training a model to learn how to learn new tasks efficiently;
• Data-level: Data-level methods focus on increasing available data to improve the generalization of the model. Performance;
• Parameter level: Parameter-level methods aim to learn more robust feature representations for better generalization to new tasks

Meta-learning

metalearning (learning how to learn). This method trains a model to learn how to learn new tasks efficiently. This model is about identifying commonalities between different tasks and using this knowledge to learn new things quickly with a few examples.

Meta-learning algorithms typically train a model on a set of related tasks and learn to extract task-independent features and task-specific features from the available data. Task-independent features capture general knowledge about the data, while task-specific features capture the details of the current task. During training, the algorithm learns to adapt to new tasks by updating the model parameters using only a few labeled examples of each new task. This allows the model to generalize to new tasks with few examples.

Data-level methods

Data-level methods focus on expanding existing data, which can help the model better understand the underlying structure of the data, thereby improving the generalization performance of the model.

The main idea is to create new examples by applying various transformations to existing examples, which can help the model better understand the underlying structure of the data.

There are two types of data-level methods:

• Data augmentation: Data augmentation involves creating new examples by applying different transformations to existing data;
• Data generation: Data generation involves generating new examples from scratch using generative adversarial networks (GANs).

Data-level methods:

The goal of parameter-level methods is to learn more robust feature representations that can better generalize to new tasks.

There are two parameter-level methods:

• Feature extraction: Feature extraction involves learning a set of features from the data that can be used for new tasks;
• Fine-tuning: Fine-tuning involves adapting a pre-trained model to a new task by learning optimal parameters.

For example, suppose you have a pre-trained model that can recognize different shapes and colors in images. By fine-tuning the model on new data sets, it can quickly learn to recognize new categories with just a few examples.

Meta-learning algorithm

Meta-learning is a popular approach to FSL, which involves training a model on a variety of related tasks so that it can learn how to learn new tasks efficiently. The algorithm learns to extract task-independent and task-specific features from available data, quickly adapting to new tasks.

Meta-learning algorithms can be broadly divided into two types: metric-based and gradient-based.

Metric-based meta-learning

Metric-based meta-learning algorithms learn a special way to compare different examples for each new task. They achieve this by mapping input examples into a special feature space, where similar examples are close together and dissimilar examples are far apart. The model can use this distance metric to classify new examples into the correct category.

A popular metric-based algorithm is Siamese Network, which learns to measure the distance between two input examples by using two identical subnetworks. These sub-networks generate feature representations for each input example and then compare their outputs using distance measures such as Euclidean distance or cosine similarity.

Gradient-based meta-learning

Gradient-based meta-learning learns how to update their parameters so that they can quickly adapt to new challenges.

These algorithms train models to learn an initial set of parameters and quickly adapt to new tasks with just a few examples. MAML (model-agnostic meta-learning) is a popular gradient-based meta-learning algorithm that learns how to optimize the parameters of a model to quickly adapt to new tasks. It trains the model through a series of related tasks and updates the model's parameters using some examples from each task. Once the model learns these parameters, it can fine-tune them using other examples from the current task, improving its performance.

Image classification algorithm based on few-shot learning

FSL has several algorithms, including:

• Model-Agnostic Meta-Learning ): MAML is a meta-learning algorithm that learns a good initialization for the model and can then adapt to new tasks with a small number of examples.
• Matching Networks: Matching networks learn to match new examples with labeled examples by calculating similarities.
• Prototypical Networks: Prototypical Networks learn prototype representations for each class and classify new examples based on their similarity to the prototypes.
• Relation Networks: Relation networks learn to compare pairs of examples and make predictions about new examples.

Model-agnostic meta-learning

The key idea of ​​MAML is to learn the initialization of model parameters that can be adapted to new tasks with a few examples. During training, MAML accepts a set of related tasks and learns to update model parameters using only a few labeled examples of each task. This process enables the model to generalize to new tasks by learning good initializations of model parameters that can be quickly adapted to new tasks.

Matching network

Matching network is another commonly used few-shot image classification algorithm. Instead of learning fixed metrics or parameters, it learns dynamic metrics based on the current support set. This means that the metric used to compare the query image and the support set differs for each query image.

The matching network algorithm uses an attention mechanism to calculate a weighted sum of the support set features for each query image. Weights are learned based on the similarity between the query image and each support set image. The weighted sum of the support set features is then concatenated with the query image features, and the resulting vectors are passed through several fully connected layers to produce the final classification.

Prototype Network

Prototype Network is a simple and effective few-sample image classification algorithm. It learns a representation of the image and computes a prototype for each class using the average of the embedding features of the supporting examples. During testing, the distance between the query image and each class prototype is calculated and the class with the closest prototype is assigned to the query.

Relationship Network

Relationship network learning compares pairs of examples that support sets and uses this information to classify query examples. The relationship network includes two sub-networks: feature embedding network and relationship network. The feature embedding network maps each example in the support set and the query example into a feature space. The relation network then computes the relation score between the query example and each support set example. Finally these relationship scores are used to classify the query examples.

Applications of few-shot learning

Few-shot learning has many applications in different fields, including:

In various computer vision tasks, including image classification and target detection and segmentation. Few-shot learning can identify new objects in images that are not present in the training data.

In natural language processing tasks, such as text classification, sentiment analysis, and language modeling, few-shot learning helps improve the performance of language models on low-resource languages.

Using minority learning in robotics enables robots to quickly learn new tasks and adapt to new environments. For example, a robot can learn to pick up new objects with just a few examples.

Few samples are used in medical diagnostics to identify rare diseases and abnormalities when data is limited, and can help personalize treatments and predict patient outcomes.

Summary

Few-shot learning is a powerful technique that enables a model to learn from a small number of examples. It has numerous applications in various fields and has the potential to revolutionize machine learning. With continued research and development, few-shot learning can pave the way for more efficient and effective machine learning systems.

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