Overview of Few-Shot Learning, Algorithm and Implementation Examples

Overview

Few-Shot Learning is a technique aimed at correctly classifying or predicting new classes or tasks using only a small number of training examples. It is particularly useful in application areas where data is limited, such as image recognition, natural language processing (NLP), speech recognition, and medical diagnosis.

The core idea of this approach is to pretrain a model on a large amount of general-purpose data, and then fine-tune or adapt it using only a small amount of task-specific data. This allows the model to quickly learn new concepts without requiring large amounts of labeled data, while still achieving high performance.

This approach closely resembles how humans learn. For instance, a child can recognize an animal after seeing only a few pictures—Few-Shot Learning seeks to replicate this kind of learning behavior in machines.

There are several major approaches to realizing Few-Shot Learning, each with its own methodology and suitable use cases.

The first is Meta-Learning, also known as “learning to learn.” As discussed in *“Overview and Implementation Examples of Meta-Learners for Few-shot/Zero-shot Learning,”* this approach aims to acquire general learning strategies by training across many different tasks. Representative algorithms include **MAML (Model-Agnostic Meta-Learning)** and **Prototypical Networks**.

The second approach is **Transfer Learning**, which is covered in detail in *“Overview and Algorithms of Transfer Learning and Its Implementation Examples.”* This method reuses models that have been pre-trained on large datasets for new tasks. In NLP, it is particularly common to utilize large pre-trained language models such as **GPT** (see *“Overview and Algorithms of GPT and Its Implementation Examples”*) and **BERT** (see *“Overview and Algorithms of BERT and Its Implementation Examples”*), which can adapt to new tasks with only a small amount of data.

Lastly, there is the **Memory-Augmented Model** approach. These models incorporate a memory component that stores previous examples and enables inference by comparing new inputs against stored instances. Typical examples include **Matching Networks** and **Siamese Networks**.

Each of these approaches tackles the challenges of Few-Shot Learning from a different angle, and their use should be chosen based on the nature of the task and available data.

Few-Shot Learning offers several advantages, but it also comes with challenges that must be addressed.

One of its major **advantages** is the ability to learn from only a few examples. Unlike traditional deep learning, which requires massive amounts of labeled data, Few-Shot Learning can build useful models from limited datasets. This drastically reduces the cost of data collection and annotation. In addition, its ability to quickly adapt to new tasks makes it a highly flexible approach.

However, **challenges** remain. The accuracy of models can be unstable due to the limited amount of data, making them more susceptible to noise and bias. Class imbalance can also significantly affect performance, necessitating careful data design. Furthermore, in the case of meta-learning approaches, the training process itself can be computationally expensive, requiring considerable time and resources.

In summary, Few-Shot Learning is a powerful and efficient approach, offering high adaptability, but it also demands thoughtful strategies to overcome issues related to accuracy and computational cost.

Related Algorithms

Few-Shot Learning (FSL) involves various representative algorithms that can be categorized by approach. Since FSL aims to achieve generalization from only a few examples, methods based on similarity, meta-learning, and pretraining with fine-tuning are commonly used.

1. Similarity-Based Algorithms (Metric-Based)

• Siamese Networks: An architecture that learns similarity (distance) between two input data points. It performs distance-based classification and is particularly effective for one-shot classification.

• Matching Networks: Uses episodic training and computes similarity with a support set to perform classification. It is known for high task adaptability.

• Prototypical Networks: Calculates a prototype (mean vector) for each class and classifies based on the distance to these prototypes. It is simple and fast.

• Relation Networks: Learns a relationship score between the input and each class using a neural network. Classification is based on the strength of these relationships.

2. Meta-Learning Based

• MAML (Model-Agnostic Meta-Learning): A general-purpose meta-learning method that learns initial model parameters that can be quickly adapted to new tasks using only a few data points.

• Reptile: A simplified approximation of MAML that performs meta-learning without requiring second-order gradients. It is computationally efficient and easy to implement.

• LSTM Meta-Learner: An innovative approach that uses an LSTM as an optimizer, learning the parameter update process itself.

• FOMAML (First-Order MAML): A faster variant of MAML that simplifies gradient computations using only first-order derivatives, suitable for efficient implementation.

3. Memory-Augmented Methods

• Memory-Augmented Neural Networks (MANN): A neural architecture augmented with external memory, enabling storage and retrieval of knowledge from a few examples.

• Neural Turing Machine (NTM): A neural network with external memory that can read and write data like a database, offering flexible inference and memory capabilities.

4. Pretraining + Fine-Tuning Based

• Transfer Learning: Adapts a model pretrained on large-scale data by fine-tuning it on a new task with a small dataset.

• GPT-3 / GPT-4 Few-Shot Prompting: Performs inference without additional training by embedding a few-shot example set directly into the prompt.

• T5 / BERT + Adapter Tuning: Efficient few-shot learning method that adds lightweight adapter layers to a pretrained model and fine-tunes only those parameters.

5. Other Notable Approaches

• Meta-SGD: Learns not only the initialization of parameters but also the learning rates, allowing fast adaptation to few-shot tasks.

• ProtoMAML: A hybrid model that combines the strengths of Prototypical Networks and MAML. It uses prototype initialization with meta-learning updates.

• FEAT (Few-shot Embedding Adaptation with Transformer): A powerful approach that dynamically adapts the embedding space per task using a Transformer, enabling flexible class representations.

Recommended Algorithms by Application Area

• Image Classification:

  • Prototypical Networks

  • Matching Networks

• NLP (Natural Language Understanding):

  • GPT-4 Few-Shot Prompting

  • T5 Adapter

• Meta-Learning Research:

  • MAML

  • Reptile

  • Meta-SGD

• Memory-Intensive Tasks:

  • Memory-Augmented Networks

  • Neural Turing Machines (NTMs)

Implementation examples

Here we describe a simple implementation (based on PyTorch) of a typical Few-Shot Learning algorithm, Prototypical Networks. It is often applied to the Few-Shot task of image classification.

Composition

N classes (N-ways) for each task, K examples (K-shots) for each class

Separate training for support set and query set

Step 1: Environment/Import

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader

Step 2: Embedded network (e.g., a simple CNN)

class ConvEmbeddingNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(1, 64, 3, padding=1),  # input: 1x28x28 (MNIST)
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 64, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Flatten(),
        )

    def forward(self, x):
        return self.encoder(x)

Step 3: Prototype Calculation and Classification Logic

def compute_prototypes(support_embeddings, support_labels, n_classes):
    # Calculate the average for each class
    prototypes = []
    for cls in range(n_classes):
        cls_embeddings = support_embeddings[support_labels == cls]
        prototype = cls_embeddings.mean(dim=0)
        prototypes.append(prototype)
    return torch.stack(prototypes)

def euclidean_distance(a, b):
    # a: [N_query, D], b: [N_class, D]
    return ((a.unsqueeze(1) - b.unsqueeze(0)) ** 2).sum(2)

Step 4: Learning loop (pseudo code)

model = ConvEmbeddingNet()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

for episode in range(num_episodes):
    support_x, support_y, query_x, query_y = sample_few_shot_task()  # custom function

    support_x, query_x = support_x.to(device), query_x.to(device)
    support_y, query_y = support_y.to(device), query_y.to(device)

    support_embeddings = model(support_x)
    query_embeddings = model(query_x)

    prototypes = compute_prototypes(support_embeddings, support_y, n_way)

    dists = euclidean_distance(query_embeddings, prototypes)
    log_probs = -F.log_softmax(dists, dim=1)
    loss = F.nll_loss(log_probs, query_y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Omniglot, miniImageNet, and MNIST are often used as sample datasets for these.

Application Examples

1. Medical Imaging

• Use Case: Classification and detection of rare diseases in X-ray or MRI images

• Reason: Since images of rare diseases are difficult to collect in large quantities, Few-Shot approaches are highly effective

• Techniques Used: Prototypical Networks, MAML, Transfer Learning

• Example: Detecting pneumonia or tumors, classifying skin cancer — quickly adapting to new disease classes with just a few annotated images

2. Natural Language Processing (NLP)

• Use Case: Translation of new languages, classification of new user intents, adding new FAQ responses

• Techniques Used: GPT-based Few-Shot Prompting, T5, BERT + Adapter Tuning

• Example: Automatically classifying unknown email formats using only a few lines of input-output examples in a prompt

3. Computer Vision (Image Classification)

• Use Case: Identification of new products or components, handwritten character recognition, logo classification

• Datasets: Omniglot, miniImageNet, proprietary product images

• Techniques Used: Matching Networks, Prototypical Networks, Relation Networks

• Example: A new product is photographed and registered with just a few images — it can be immediately recognized by a quality control system on a factory line

4. E-commerce & Recommendation Systems

• Use Case: Recommendation for new products, behavior prediction for new users

• Techniques Used: Few-Shot Recommendation Systems, Meta-Learning-based Recommendation Models

• Example: When a new T-shirt brand is launched, the system suggests similar fashion items based on just a few user clicks

5. Robotics & Reinforcement Learning

• Use Case: A robot learns how to manipulate a new object after seeing it only a few times

• Techniques Used: Meta-RL (Meta-Reinforcement Learning), MAML-based methods

• Example: A robot sees a bottle of unknown shape and learns how to grasp it after only a few trials — quickly acquiring an appropriate manipulation strategy

6. Generative AI & Creative Assistance

• Use Case: Generating images, text, or music in a specific style based on a few examples

• Techniques Used: Few-Shot Style Transfer, Fine-tuned Diffusion Models

• Example: Given just three Hokusai-style illustrations, the system generates new artwork in a similar style — useful for artistic expression and design support

Reference Materials for Few-Shot Learning

1. Introductory to Intermediate-Level Learning Resources

• Meta-Learning: A Survey (Hospedales et al., 2021, Paper)

• Deep Learning for Coders with fastai and PyTorch (Jeremy Howard)

• “Deep Learning from Scratch“ (Yusuke Saito, Japanese)
  

2. Specialized Books on Few-Shot / Meta-Learning

• Meta-learning approaches for few-shot learning: A survey of recent advances

• Meta-Learning: Theory, Algorithms and ApplicationsHands-On One-shot Learning with Python

• Hands-On One-shot Learning with Python

3. Original Research Papers (Recommended Readings)

• Siamese Networks

  • Koch et al.: Siamese Neural Networks for One-shot Image Recognition (2015)

• Prototypical Networks

  • Snell et al.: Prototypical Networks for Few-shot Learning (2017)

• Matching Networks

  • Vinyals et al.: Matching Networks for One-Shot Learning (2016)

• MAML (Model-Agnostic Meta-Learning)

  • Finn et al.: Model-Agnostic Meta-Learning for Fast Adaptation (2017)