What is Few-shot learning?

In this era, artificial intelligence and machine learning have taken over many menial tasks. These are supported by several different techniques and approaches that help efficiently and reliably predict data. For this process to run smoothly, we must have a well-trained model that can output a value correlating to the accurate information without a huge difference. We have to have an overview of what a model is and how it works to predict such accurate results.

A model refers to a mathematical representation of a system or a problem. It is typically an algorithm or a set of algorithms designed to learn patterns, make predictions, or perform tasks based on input data. Some of the most common models used in this field are:

• Neural networks

• Decision trees

• Naive Bayes classifiers

• Gaussian mixture models (GMM)

• K-nearest neighbors (KNN)

• Linear regression models

To understand what we are going to discuss in this Answer, we specifically have to go over the training part of the model.

What is model training?

Usually, training a model means providing the model with a set of labeled data to reference the predictions and correct their accuracy. The training process continues until the model reaches a satisfactory level of convergenceThe state where a model's parameters have stabilized, resulting in minimal changes to its performance or loss function during training.. The specific training duration depends on factors such as the complexity of the problem, the size of the dataset, and the chosen optimization algorithm. Typically, the dataset over which the model is trained is vast enough for the model to be trained accurately. However, the learning approach we are discussing tackles the problems if we have a limited amount of training data. The approach we are going over is called Few-shot learning.

Few-shot learning

Few-shot learning is a subfield of machine learning and artificial intelligence that deals with the challenge of learning new concepts or tasks with limited training examples. In traditional machine learning, models typically require a large amount of labeled training data to achieve good performance and accuracy. However, in real-world scenarios, acquiring a large amount of labeled data for every new concept or task may be impractical or expensive.

Before we go into the intricacies of Few-shot learning, we should have an understanding of what labeled data is.

What is labeled data?

Labeled data refers to data that has been manually annotated or labeled with specific class or category information. Each data instance in the labeled dataset is associated with a corresponding label or ground truth value, indicating its correct class or category. This type of data is crucial in machine learning and artificial intelligence for several reasons:

• Supervised learning: Models learn patterns and make predictions based on labeled examples, associating features with specific classes or categories.

• Model training and evaluation: Labeled data optimizes model parameters and evaluates performance by comparing predictions to the correct labels.

• Feature learning: Labeled data helps identify relevant patterns, capturing discriminative information for classification and regression tasks.

Labeled data is a fundamental component of training a model to predict values accurately. It enables models to associate patterns, features, or characteristics with specific labels, facilitating the understanding and decision-making capabilities of AI systems. Some of the domains where it is used are:

• Image Classification

• Text Classification

• Object Detection

How Few-shot learning works

Few-shot learning aims to address the limited dataset size issue by developing techniques that enable models to generalize and learn from only a few examples. The goal is to allow a model to quickly adapt and make accurate predictions on new classes with minimal training instances.

There are various approaches to Few-shot learning, but one standard method involves using a support set and a query set. Let’s go over the essential terms related to Few-shot learning.

• Support set: It contains a small number of labeled examples for each new class or task.

• Query set: It contains unlabeled instances that the model needs to classify or generate predictions for.

• N-way K-shot scheme: It refers to the problem statement where a model needs to generalize to N novel categories with only K labeled samples for each category. A higher N value indicates a more challenging task, while a lower K value leads to lower accuracy as there is less supporting information available for inference.

• Similarity function: It refers to a function that maps the similarities between the classes in the support and query sets while providing a probability value for the similarity as an output.

Now, Few-shot learning operates by training a similarity function that assesses the similarities between classes in the support and query sets. This function assigns probability values to indicate similarity levels to each data point. The parameters of this similarity function are trained using a large-scale labeled dataset, typically through supervised learning. The pre-trained deep model and its similarity function can then be applied in the few-shot learning phase. By leveraging the support set information, the similarity function determines similarity probabilities for the query set samples. The few-shot model predicts the class label for each query sample based on the highest similarity value from the support set. Although the ideal scenario is perfect similarity values of 1.0 for same-class images and 0.0 for different-class images, in reality, there may be slight variations in the similarity values.

Popular Few-shot learning methods

Metric-based learning

Metric-based learning is a powerful approach that trains a similarity metric or distance function to calculate the similarity between support and query instances. This method allows the model to make predictions by comparing query samples to the support set, leveraging nearest neighbors or similarity scores. By refining this technique, we can enhance the model's ability to accurately recognize patterns and classify new instances with improved efficiency and effectiveness.

Meta-learning

Meta-learning, or learning-to-learn, is a powerful technique where a model is trained on various tasks using limited examples. By doing so, the model becomes adept at generalizing across tasks and quickly applying its acquired knowledge. This is achieved by training a meta-learner or an outer loop that enhances the model's ability to adapt effectively to unfamiliar tasks. Through meta-learning, models can rapidly learn and adapt in dynamic environments.

Data-level learning

This learning approach involves adding more data, either structured or unstructured, to the support set for training the model. Techniques such as data augmentation can provide additional information to enhance performance. Generative networks like GANs can also generate new data, but they require a large amount of labeled training data.

Parameter-level learning

This learning approach addresses the limited availability of samples by using meta-learning and regularization techniques. These methods control the model's parameter space and focus on identifying essential features for the task, reducing the risk of overfitting.

Let's take a small quiz for a better understanding.

Conclusion

Few-shot learning has applications in various domains, such as computer vision, natural language processing, and robotics. It enables systems to learn new concepts quickly, adapt to changing environments, and generalize knowledge from limited data. It is a valuable technique in scenarios where acquiring abundant labeled data is challenging or infeasible.