What is Few-Shot Learning? Unlocking Insights with Limited Data

Few-shot learning (FSL) is changing data science by allowing models to make correct predictions using very little labeled data. Unlike traditional guided learning, which needs a lot of data, Few-Shot Learning (FSL) is about learning from just a few examples. This makes FSL perfect for situations where data is limited or difficult to get.

In this blog, we’ll explore Few-shot learning, its main ideas, and how it differs from traditional learning methods. This will help you understand its ability to transform various industries.

What is Few-Shot Learning?

Few-shot learning (FSL) is a type of machine learning that helps models make good predictions using very little labeled data. It is different from traditional supervised learning. While traditional methods require large amounts of labeled data for each category, FSL trains models using only a few examples, sometimes just one or two for each category.

Few-shot learning is designed to replicate how humans can quickly understand new ideas with little experience. For example, if a person sees a few pictures of a rare animal, they can usually identify that animal in other situations without needing to see hundreds of cases. FSL uses this idea to help with situations where it is hard, costly, or almost impossible to collect data, like:

• Finding rare diseases when there isn’t much medical image data available.
• Identifying different handwriting styles or marks.
• Identifying rare or newly found species when there are few samples available.

Few-shot learning is part of a larger category called n-shot learning, which has different types, including:

• One-shot learning: Learning from a single named example per class.
• Zero-shot learning: A way to make predictions without using labeled examples. It uses extra information, such as descriptions, to help make those predictions.

Instead of just classifying known examples, FSL tries to learn how to learn. This means teaching models to recognize how data points are connected and different from each other. For instance, even if the model hasn’t been trained on specific types like squirrels or pangolins, it can still determine if two pictures are from the same group by examining their features.

To learn more about advanced AI topics, check out our blog on What is Generative AI.

Now that we understand what few-shot learning is and why it’s important, let’s take a look at the key prerequisites to get started with it.

Prerequisites

Before you begin with few-shot learning, make sure you have the following:

• Access to a High-Powered GPU: Use a strong NVIDIA GPU, like the H100 or A100-80G, to run deep learning models effectively. Weaker GPUs can be used, but they might work more slowly. Learn more about GPU requirements for deep learning from NVIDIA.
• Familiarity with Python: You should have a basic knowledge of Python programming. You’ll need to use Python code to try out and test few-shot learning models.
• Beginner-Level Knowledge of Deep Learning: Learning the basic ideas of deep learning, like neural networks, activation functions, and reverse propagation, will help you understand how few-shot learning models operate. Check out this Deep Learning Guide from Edureka to get started.
• Access to Cloud-Based Resources (Optional): If you don’t have a powerful GPU, you might want to use cloud services. These on-demand GPU virtual machines offer another way to perform the necessary tasks.

Follow a detailed guide to set up a GPU Droplet, connect to it via SSH, and set up an environment (e.g., Jupyter Notebook) for coding and visualization.

Before diving into Few-Shot Learning, explore our Generative AI Course for foundational AI knowledge.

With the prerequisites in place, let’s explore why few-shot learning is gaining traction and how it addresses the limitations of traditional learning methods.

Why Few-Shot Learning?

Traditional supervised learning relies heavily on large sets of labeled data to be accurate. This method has some drawbacks, such as requiring a lot of time, effort, and computer power to gather, label, and handle the data. Moreover, these models struggle with domain shift, where the statistical distribution of training data varies from that of new data (e.g., images from a mobile phone versus those from a DSLR camera).

Few-shot learning addresses these challenges by:

• Eliminating the Dependence on Extensive Labeled Data: FSL decreases the requirement for big and expensive labeled datasets, so models can learn well using just a few labeled examples.
• Reducing Computational Costs: FSL uses pre-trained models (like those learned on ImageNet) so that you don’t have to start training models from the beginning. This reduces the use of computer resources and time while allowing the model to handle new groups.
• Recognizing Rare Categories: Few-shot learning is great for situations with little data, such as identifying rare animals or newly found species. These models can learn to identify rare groups even when little information is available.
• Adaptability to Different Data Domains: Even when trained on data with a different statistical distribution, FSL models can effectively generalize to new domains as long as the support set (a few labeled examples) and query set (unlabeled data to be classified) remain consistent.

Few-shot learning is a helpful method that addresses some problems with traditional supervised learning. It allows models to be trained well even when there isn’t much data available and can be used in different areas.

For a broader understanding of how AI is transforming industries, you can read about Artificial Intelligence Applications.

Now that we understand why Few-Shot Learning is important, let’s dive into how it actually works and the key techniques behind it.

How does Few-Shot Learning work?

Few-shot learning (FSL) focuses on creating a function that determines how similar classes are in two groups: the support set and the question set. This function checks how alike or different data sets are. The result of this similarity function usually shows a chance, which indicates how likely it is that two samples are in the same category.

Similarity Measure in Few-Shot Learning

Let’s look at a situation involving electronic gadgets. In an ideal situation, two pictures of the same smartphone model should have a similarity score of 1.0, showing they look the same.

If you compare a picture of a smartphone to a picture of a laptop, they should have a similarity score of 0.0 because they are very different products.

In reality, slight differences can occur because of things like lighting, angles, or backgrounds. For example:

• Two pictures of the same smartphone: They may have a similarity number of about 0.98, which means they are almost the same but there are some small differences.
• When you compare a smartphone picture to a laptop image: They might get very low scores, around 0.05 or 0.07, because they are so different in design, size, and features.

This measure of closeness is very important in few-shot learning. The aim is to determine which category a sample from the question set fits into by comparing it with samples in the support set.

Training the Similarity Function

A big-named dataset like ImageNet is used to teach the model how to understand similarities in a supervised way. When this pre-trained model is ready, it can help in Few-Shot Learning by predicting new samples based on their similarity to the reference set.

Using a Siamese Network for Similarity Functions

In Few-Shot Learning, we often use neural networks to compare similarities, and one well-known model for this is the Siamese Network. Siamese describes a type of network made up of two or more similar neural networks that use the same settings. These networks handle data at the same time and then check the results.

Method-1: Pairwise Similarity

In this method, the Siamese network is taught with two examples from the dataset. If the samples belong to the same class, the model assigns a closeness label of 1.0, and if they belong to different classes, it assigns a label of 0.0. This method teaches the network how to measure similarity by using examples that are already named. After the samples go through the pre-trained feature generator, the network measures how similar they are and then adjusts its settings using backpropagation.

Here’s a small snippet of code to calculate the cosine similarity between two samples:

import torch
import torch.nn as nn

input1 = torch.randn(100, 128)
input2 = torch.randn(100, 128)
cos = nn.CosineSimilarity(dim=1, eps=1e-6)
output = cos(input1, input2)

In Python, you can use the SiameseDataset class to manage datasets. This class helps you load pairs of pictures that are either from the same category (positive samples) or from different categories (negative samples).

import random
from PIL import Image
import torch
from torch.utils.data import Dataset

class SiameseDataset(Dataset):
def __init__(self, folder, transform=None):
self.folder = folder # Type: torchvision.datasets.ImageFolder
self.transform = transform # Type: torchvision.transforms

def __getitem__(self, index):
# Random image set as anchor
image0_tuple = random.choice(self.folder.imgs)

random_val = random.randint(0, 1)
if random_val: # If random_val = 1, output a positive class sample
while True:
# Find "positive" Image
image1_tuple = random.choice(self.folder.imgs)
if image0_tuple[1] == image1_tuple[1]:
break
else: # If random_val = 0, output a negative class sample
while True:
# Find "negative" Image
image1_tuple = random.choice(self.folder.imgs)
if image0_tuple[1] != image1_tuple[1]:
break

image0 = Image.open(image0_tuple[0])
image1 = Image.open(image1_tuple[0])

if self.transform is not None:
image0 = self.transform(image0)
image1 = self.transform(image1)

# Return the two images and their similarity label
return image0, image1, int(random_val)

def __len__(self):
return len(self.folder.imgs)

Method-2: Triplet Loss

In the Triplet Loss method, the model uses three images instead of just two. These images are: an anchor image, a positive image (which is from the same category as the anchor), and a negative image (which is from a different category). The aim is to bring the anchor and positive samples closer in the embedding area and move the anchor and negative samples further apart.

Here’s how the TripletMarginLoss is implemented in PyTorch:

import torch
import torch.nn as nn</pre>
triplet_loss = nn.TripletMarginLoss(margin=1.0, p=2)
anchor = torch.randn(100, 128, requires_grad=True)
positive = torch.randn(100, 128, requires_grad=True)
negative = torch.randn(100, 128, requires_grad=True)
output = triplet_loss(anchor, positive, negative)
output.backward()

To handle the dataset for the triplet loss method, you’ll need to change the class so that it gives you triplets, which include an anchor, a positive sample, and a negative sample.

import random
from PIL import Image
import torch
from torch.utils.data import Dataset

class TripletDataset(Dataset):
def __init__(self, folder, transform=None):
self.folder = folder # Type: torchvision.datasets.ImageFolder
self.transform = transform # Type: torchvision.transforms

def __getitem__(self, index):
# Random image set as anchor
anchor_tuple = random.choice(self.folder.imgs)

while True:
# Find "positive" Image
positive_tuple = random.choice(self.folder.imgs)
if anchor_tuple[1] == positive_tuple[1]:
break

while True:
# Find "negative" Image
negative_tuple = random.choice(self.folder.imgs)
if anchor_tuple[1] != negative_tuple[1]:
break

anchor = Image.open(anchor_tuple[0])
positive = Image.open(positive_tuple[0])
negative = Image.open(negative_tuple[0])

if self.transform is not None:
anchor = self.transform(anchor)
positive = self.transform(positive)
negative = self.transform(negative)

return anchor, positive, negative

def __len__(self):
return len(self.folder.imgs)

Few-shot learning is a useful method that allows models to make correct predictions using very little data by learning how things are similar. Methods like Siamese Networks and Triplet Loss are important for making Few-Shot Learning effective, and they can be used in many different areas.

To expand your knowledge on similar AI models, check out this Generative AI Tutorial.

Now that we understand the fundamentals of few-shot learning, let’s dive into how few-shot classification specifically operates.

How does a Few-Shot Learning classification Work?

Few-shot learning (FSL) focuses on making correct guesses using only a small amount of labeled data. It usually uses transfer learning and meta-learning strategies, or a combination of both.

Most FSL methods are about classification, which means their main goal is to identify new categories using only a few examples. Here’s how these methods are important:

1. Transfer Learning

Transfer learning means using a model that has already learned from a big set of data and adjusting it to perform a new, similar task with less data. This method uses what the model has learned to prevent overfitting, a frequent problem when working with small datasets. For example, a model that learns to recognize different types of birds might only need a few extra labeled images to identify a new type because it already knows important things like feathers and beaks. Tweaking the model or changing its design helps it perform better on new tasks.

2. Data-Level Approaches

To address the lack of named data, we use methods like data augmentation or generative models, such as GANs or VAEs. These methods create extra samples similar to the original data, which helps the model work better without needing more real data. For example, in medical applications or for identifying rare species, these methods help create more examples for training.

3. Meta-Learning

Meta-learning, or learning to learn, is a way of training a model on many tasks so it can find a general method to solve new tasks using less data. Instead of teaching a model for just one task, meta-learning helps the model learn how to quickly adjust to new tasks by spotting similarities in different areas. This helps the model apply prior information to unseen data, which is especially useful in few-shot classification tasks.

4. N-way-K-shot Classification

Few-shot learning usually uses the N-way-K-shot method. In this setup, N is the number of classes, and K is the number of cases for each class. In a 3-way-2-shot job, the model receives 2 examples from each of 3 different groups and is then asked to classify new examples using these few samples. This setup helps the model learn and make predictions with only a few named examples, which is important when data is scarce.

Few-shot classification uses techniques like transfer learning, data enrichment, and meta-learning to help models perform well even with a small amount of labeled data. Using methods like N-way-K-shot, these models can learn from small amounts of data and quickly adjust to new jobs.

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Now that we’ve covered how few-shot classification works, let’s explore one of the key techniques used in this area: Metric-Based Meta-Learning.

Metric-Based Meta-Learning

Metric-based meta-learning algorithms focus on figuring out how similar data samples are instead of creating models to separate different groups. These methods create continuous values, known as vector embeddings, to describe data points. They then develop a function to measure the distance between these points to make predictions.

Siamese Networks

Siamese networks are one of the first methods that use a metric-based approach to solve binary classification problems through contrastive learning. The network compares pairs of samples to guess whether they match. The aim is to make the distance between matching pairs of vector embeddings as small as possible, while making the distance between non-matching pairs as large as possible. This method works well when the training samples are hard to tell apart, often needing extra data to improve the outcome.

Matching Networks

Matching networks build on Siamese networks to allow for classifying items into multiple categories. This method creates an embedding for each sample in both the support and query sets. It predicts the classification by comparing the cosine distance between the embedding of the question sample and the embeddings in the support set. It’s one of the first methods created for learning with only a few examples.

Prototypical Networks

Prototypical networks use a unique method of finding an average model (called a prototype) for each class using all the available samples. A data point is categorized by how close it is to these example points using a method called Euclidean distance. This method has been improved by using techniques like label propagation to improve the testing process.

Relation Networks

Relation networks work like matched and prototypical networks, but they use a different method to calculate the distance between embeddings. Other methods use set distance functions, like cosine or Euclidean distance. In contrast, relation networks use a special module that learns the best way to measure distance for a specific classification job, which makes them more flexible and accurate.

While metric-based meta-learning focuses on comparing data through similarity measures, optimization-based meta-learning takes a different approach. It fine-tunes model parameters to enable rapid adaptation to new tasks.

Optimization-Based Meta-Learning

Optimization-based meta-learning aims to improve neural network’s ability to learn quickly from just a few examples by improving their training process. Traditional deep learning uses a method called gradient descent to make changes repeatedly. It also needs a lot of labeled data to work effectively. On the other hand, optimization-based methods try to reduce the number of changes required by improving the training process itself.

Key Optimization-Based FSL Methods

1. Model Agnostic Meta-Learning (MAML)

MAML is a well-known method that can be used with different types of AI models. It adjusts a model’s starting settings so they can be easily updated for new jobs. The process includes two steps for updating parameters:

• Task-Specific Updates: Change the settings using gradient descent for each selected job.
• Meta-Parameter Updates: Improve starting settings according to updates for individual tasks.

Notable Variants:

• First Order MAML (FOMAML): Makes calculations easier by using only the first-order derivatives.
• Reptile: Uses special rules for updating parameters to keep things simple and efficient.
• Meta-SGD, Alpha MAML, and MAML++: They improve optimization steps and improve reliability.

2. LSTM Meta-Learner

Long-short term memory (LSTM) networks act as learners that understand both short-term tasks and long-term patterns. This method helps train classifiers to adapt better to different jobs.

3. Latent Embedding Optimization (LEO)

LEO works in a low-dimensional embedding space, optimizing parameters through a generative distribution akin to variational autoencoders (VAEs). This makes the calculations easier without losing effectiveness.

Meta-learning that focuses on optimization helps models improve how they learn new tasks. Now, let’s look at different methods that address the problems of few-shot learning, each with its way of boosting performance using very little data.

Approaches for Few-Shot Learning

Few-shot learning methods are divided into four main strategies, each focused on solving the problems that come with having very little labeled data:

1. Data-Level Approaches

Data-level methods aim to improve the dataset by adding more cases to address the lack of labeled data. Methods include:

• Data Augmentation: Improving the information by making new versions of current examples.
• Semi-Supervised Learning: Integrating unlabeled data into the reference set for better insights.
• Generative Models: Using tools like GANs to create new examples based on current data. However, they need a lot of labeled data for training at the start.

These methods add more data for training, which helps the model work better and reduces the chances of making mistakes

2. Parameter-Level Approaches

Parameter-level methods adjust the model’s settings to improve learning from small amounts of data. In this case:

• Meta-Learning: Helping the model focus on important features for each job.
• Regularization Techniques: Limiting the range of parameters to avoid overfitting when dealing with data that has many dimensions.

These methods allow for accurate predictions even with few samples by smartly managing the parameters.

3. Metric-Level Approaches

These methods depend on measuring lengths between data points in the embedding space. Some common methods are:

• Distance Metrics: Measuring likeness using Euclidean distance, Cosine likeness, or Earth Mover Distance.
• Feature Extraction: Teaching models to find important traits and improve distance calculations for more accurate results.

Metric-level methods are great for finding connections between query and reference samples using similarity scores.

4. Gradient-Based Meta-Learning

Gradient-based methods use a teacher-student setup to improve the model’s settings.

• Teacher Model: Trained with support sets to make predictions on question samples.
• Student Model: Learns from the teacher’s feedback to improve its classification skills with little data.

This two-part learning method helps explore different options effectively and improves how well tasks can be adjusted.

Let’s explore a tougher version of Few-Shot Learning, called One-Shot Learning. In this version, we have only one case for each class.

One-Shot Learning

One-shot learning is challenging because there is only one example for each class in the support set. It requires effective learning methods with little help or background knowledge. For example, the face recognition feature in today’s smartphones uses One-Shot Learning.

An example of a good method is the One-Shot Semantic Segmentation technique. This method uses a special design with two branches:

• Parameter Generation Branch: This part takes a labeled picture and produces a set of parameters.
• Segmentation Branch: This branch uses these values along with a new image to create a segmentation mask for the new class.

This design has several benefits compared to standard fine-tuning methods:

• Speed: The network calculates factors in one go, which makes it quicker.
• Differentiability: It can be fully differentiated, which allows us to train the parameter generation and segmentation parts together.
• Scalability: The amount of parameters does not change with the size of the picture, so performance stays good regardless of size.

One-Shot Learning is a type of Few-Shot Learning that works well with little data. It has many uses in different areas, changing how we handle jobs in computer vision, robotics, and natural language processing.

Applications of Few-Shot Learning

Few-Shot Learning (FSL) has become important in many areas because it works well even when there isn’t much data available. Let’s look at some important ways FSL has been used successfully:

1. Image Classification

Few-shot learning is commonly used in image classification, providing new ways to handle problems when little classified data is available.

• For example, Zhang and others created a method that uses Earth Mover’s Distance (EMD) to compare similar structures in picture features. This method effectively matches parts of two images, reducing prices and improving classification accuracy.
• EMD offers a strong way to compare structural data, which helps FSL models do well with real-world picture classification tasks.

2. Finding Objects

Object detection finds and locates objects in an image or video. This job can be tricky because there are often many objects in one picture.

• For example, the ONCE model addressed Incremental Few-Shot Object Detection by dividing the learning process into general and specific parts for different classes.
• ONCE trains a system to recognize features in basic categories. Then, during tests, it uses a specially designed code generator to quickly adjust to new categories using only a small amount of data.
• It easily adds new categories simply and efficiently.

3. Semantic Segmentation

Semantic segmentation labels each pixel in a picture, which is useful for analyzing images in detail.

For example, Liu suggested a Semi-Supervised Few-Shot Semantic Segmentation framework with part-aware prototypes for capturing diverse object features.

Using unlabeled images helps the model better deal with differences within the same class and allows it to go beyond small sets of labeled pictures.

This allows for better and more complete separation, even when there is little labeled data.

4. Robots

Few-shot learning is being used in robotics to help robots understand and perform tasks based on only a few examples.

• For example, Wu and others created a program that helps plan movements by copying how things move in different situations. The program uses key points that stay the same to adjust the paths for new cases.
• This cuts down the number of tests needed, making learning faster and helping robots adjust better.

5. Natural Language Processing (NLP)

FSL is becoming popular in natural language processing (NLP) jobs because it can be hard and expensive to get labeled data.

• For example, Yu and others used metric learning to categorize text. They grouped similar training tasks and created special metrics for each group.
• For each FSL task, a special measurement is created by combining different cluster-based metrics. This makes sure the job is relevant and diverse.
• This helps classify text correctly with a few named examples, which is an important improvement for language processing.

To understand how Few-Shot Learning is so adaptable, it’s important to know the difference between two main parts: the Support Set and the Training Set.

Support Set vs. Training Set

In few-shot learning, the support set is a word often used in meta-learning. It means a small group of examples with labels that are used in testing to help make better guesses. This is different from the training set, which is bigger and used to train deep neural networks in regular machine learning.

And, if we talk about the differences between the Support Set and the Training Set:

The Idea Behind the Support Set

To train a deep neural network using standard machine learning, you need a lot of examples for each category. In a support set, each class might have just a few cases. This small set of data helps during tests by allowing the model to learn and make predictions based on relationships and patterns instead of just memorizing information.

Now that we’ve looked at the difference between the Support Set and Training Set, let’s talk about learn to learn and how it’s important for Few-Shot Learning.

How to understand learn to learn?

Imagine taking a child to the zoo. He spots a soft animal in the water that he’s never seen before and excitedly asks, What is this? Now, you give him a set of cards that show pictures of animals along with their names. Although the animals on the cards are new to him and he has never seen the one in the water, the child is smart enough to understand. He looks at the animal in the water and compares it to the cards to find the best match. The power to learn on his own is the core idea of meta-learning.

Here’s the twist: before going to the zoo, the child already knew how to compare animal’s similarities and differences. In this situation, the strange animal in the water is called the question, and the cards it uses to learn are called the support set. Learning to learn on your own is called meta-learning.

If the child only needs one card for each species to recognize them, that’s called one-shot learning. Meta-learning in machine learning focuses on quickly understanding and adjusting to new situations with little knowledge.

Now that we understand what learn to learn means, let’s look at how few-shot learning, an important part of meta-learning, is different from traditional guided learning.

Few-Shot Learning vs. Supervised Learning

In supervised learning, a model learns from a large set of data with labels for each piece of information. Once the model is learned, we use it to make predictions. The process is easy: We show a test sample, and the model identifies it using what it has learned from the training data.

On the other hand, few-shot learning poses a different problem. Here, the example given to the model is brand new; it comes from a category the model has never seen before. The main difference between the two methods is that in guided learning, the model learns from already-known categories, while in few-shot learning, the model needs to learn to identify new categories with just a few examples.

Now that you know the main differences between them, let’s look at some important terms that will help you understand how few-shot learning works.

Terminologies in Few-Shot Learning

Let’s explain some important terms in few-shot learning:

• k-way means the number of different classes in the support set. For example, if there are 5 classes, we call it a 5-way support set.
• n-shot refers to how many samples there are for each class in the support set. So, if each class has 3 samples, we call it a 3-shot.

In short, we describe the support set as k-way and n-shot to show how many classes (k) and how many samples for each class (n) it has.

Now that we’ve discussed the important terms in few-shot learning, let’s look at how these ideas affect prediction accuracy in these learning tasks.

Prediction Accuracy of Few-Shot Learning

When performing few-shot learning, the prediction accuracy is affected by two factors: the number of ways (k-way) and the number of shots (n-shot).

• As the number of options goes up, the accuracy of predictions often goes down.

Why does this occur? Let’s consider an example: you give a child 3 cards and ask them to choose the right one. This is a job that involves three parts and is done in one attempt. What if you let the child pick from 6 cards? That’s called 6-way 1-shot learning. Which one do you think is easier?

It’s clear that picking from three choices is easier than picking from six options. In general, learning in 3 parts is more accurate than learning in 6 parts.

• As you take more shots, the accuracy of predictions gets better. More examples help the model make better predictions. For instance, 2-shot learning is simpler than 1-shot learning because there are more examples to help make a choice.

Now that we know how prediction accuracy is affected by few-shot learning, let’s explore the main idea behind this method—how it teaches a model to recognize similarities.

The Basic Idea Behind Few-Shot Learning

The basic idea of few-shot learning is to train a function that predicts the relationship between samples.

This likeness is measured with a function called sim(x, x’), where x and x’ are two samples.

• If the data are identical, the similarity function gives a score of 1: sim(x, x’) = 1
• If the samples are not alike, the similarity function gives a number of 0: sim(x, x’) = 0

After training, this similarity function can be used to make predictions for unseen questions. We can find similarity scores by comparing the question with each sample in the support set. Next, we look for the most similar sample and use that as the expected label.

Here’s how the process works:

Let’s look at a real-life example of a traffic tracking system. The system gets a picture of a vehicle from a traffic camera and needs to find out what kind of vehicle it is. The system uses a collection of sample pictures showing various types of vehicles.

Image from the traffic camera:

Sample Images:

The system checks the query picture against each sample image and measures how similar they are. For example:

• Sedan: Similarity score of 0.85
• Truck: Similarity score of 0.50
• Motorcycle: Similarity score of 0.30
• Bus: Similarity score of 0.65

The system finds that the car in the query picture is most likely a sedan because it has a similarity score of 0.85 with the Sedan sample.

One-shot learning works by comparing a new example (query) to each example in a set of known samples (support set). The model finds the most similar example and uses it to make a prediction.

Conclusion

In summary, Few-Shot Learning is changing how models work with small amounts of data, allowing them to get great results even when there are only a few examples. FSL helps solve real-world problems where data is limited or hard to find by emphasizing the ability to generalize and learn to learn.” This field is changing and will likely be used more in healthcare, identifying rare species, and other areas. It will provide new answers to problems that traditional machine learning can’t easily solve.

Want to explore more? Start learning with our Generative AI Course or dive into a specialized Prompt Engineering Course.

Related Post: Few-shot learning in Prompt Engineering

FAQs

1. What is few-shot learning strategies?

Few-shot learning techniques help a model learn to recognize patterns using only a small number of examples. This is useful when there isn’t much labeled data available.

2. What is few-shot learning in CV?

In computer vision (CV), few-shot learning helps models recognize and classify things using just a few images, instead of needing many labeled examples.

3. What is shot learning?

Shot learning means how many examples a model gets to learn from. It is usually called one-shot, few-shot, or many-shot depending on how many cases there are.

4. What is the difference between one-shot and few-shot learning?

One-shot learning means learning from just one example, and few-shot learning means learning from a small number of examples, usually between 2 and 10.