Overview and implementation examples of Meta-Learners that can be used for Few-shot/Zero-shot Learning

Meta-Learners

Meta-Learners are one of the key concepts in the domain of machine learning and can be understood as “algorithms that learn learning algorithms”. In other words, Meta-Learners can be described as an approach to automatically acquire learning algorithms that can be adapted to different tasks and domains.

Usually, machine learning models are optimized for a specific task or domain and need to be re-trained and adjusted to apply them to new tasks. However, a metalearner has the ability to quickly adapt to new tasks by automatically acquiring effective learning algorithms for multiple tasks.

The meta-learner has the following characteristics and approaches

• Meta-learning: The meta-learner learns using data and results from multiple learning tasks. The goal of meta-learning is to extract commonalities and patterns among different learning tasks and to obtain a general model or knowledge that can be adapted to new tasks.
• Meta-features: Meta-features extracted from the input data and output results of each learning task play an important role in the meta-learner. These meta-features are used for adaptation and prediction to new tasks.
• Meta-learning architecture: Meta-learners usually have special structures and mechanisms in place when designing the architecture of their learning algorithms and models. This allows them to integrate information from different learning tasks to improve adaptability to new tasks and generalization performance.
• Few-shot/Zero-shot Learning: meta-learners are particularly useful in cases where small amounts of data (few-shot learning) or new tasks can be adapted without prior learning (zero-shot learning). By using meta-learning, high performance may be achieved even with small amounts of data and in new domains.

The main approaches to meta-learning include.

Gradient-Based Methods

Gradient-Based Methods in meta-learning are methods for obtaining adaptive learning algorithms for multiple tasks. In this method, the gradient information obtained from multiple tasks is used to generate a model suitable for the new task.

The specific steps are as follows

1. Meta-Training:
   • In the initial stage of meta-learning, several basic tasks are prepared. These basic tasks represent different datasets and tasks within the meta-learning framework.
   • For each basic task, the usual learning process (feed-forward data, compute errors, compute gradients) is performed to obtain trained model parameters for each basic task.
2. Meta-Testing:
   • As new tasks emerge, the meta-runner needs to acquire the appropriate learning algorithm to solve them.
   • During meta-testing, initialized model parameters for the new task are used.
   • Using the data from the new task, the model is updated to compute gradient information such that the error is minimized.
   • This will update the model to obtain the optimal model parameters for the new task.
3. Applying the meta-test:
   • Using the optimized model parameters obtained from the meta-test, this is used as an initialization when solving the new task.
   • The meta-runner aims to achieve high performance with less data by initializing the model in such a way that it helps the model converge for the new task.

An example of a common Gradient-Based Methods is Model-Agnostic Meta-Learning (MAML), which uses gradient information to perform meta-learning independent of the model’s architecture, and the basic idea is that the model The basic idea is to obtain a model that is adaptable to multiple tasks by adjusting the initialization method.

Gradient-Based Methods are superior in terms of data efficiency and generalization capability, but they are computationally expensive and may perform better when there is high relevance between tasks.

Model-Agnostic Meta-Learning (MAML):

MAML is an architecture-independent meta-learning approach that generates models applicable to multiple tasks by adjusting the initialization method of the learning algorithm. MAML is considered an “agnostic” approach because it can be applied independently of the model’s architecture.

The basic idea of MAML is to generate models that are adaptable to new tasks by adjusting the model initialization. An overview of the MAML methodology is given below.

1. Meta-Training:
   • In the initial phase of MAML, several basic tasks are prepared. Each of these basic tasks represents a different data set and task.
   • Each basic task is trained to obtain the model parameters for that task.
2. Computing gradients:
   • Calculate the loss for each task using the model parameters obtained for each basic task.
   • Calculate the gradient of the model parameters for the losses for each task.
3. Update model parameters:
   • Take the average of the gradient information calculated from all tasks and use it to update the parameters of the meta-model (initial model).
   • The meta-model will reflect the information obtained from the training in the basic task.
4. Meta-Testing:
   • When a new task emerges, the meta-model is used as an initialization.
   • The data from the new task is used to update the initialized model and calculate the gradients to obtain the optimal model parameters for that task.

The advantages of MAML are that it can be applied independently of the architecture of the model and that it is easy to achieve high performance with small amounts of data. However, the computational cost and the choice of hyperparameters are important to achieve high performance for many tasks.

Developed and improved versions of MAML include the following

• Reptile: Reptile is a simpler approach to the idea of MAML: MAML is fine-tuned internally for each task, whereas Reptile adapts the model to the task by fine-tuning it over a few iterations for multiple tasks.
• First-Order MAML (FOMAML): FOMAML is an approach to reduce the computational cost of MAML; MAML is considered computationally expensive because it requires second-order derivatives, whereas FOMAML uses only first-order derivatives to compute approximate metagradients.
• Learnable Evolutionary Optimization (LEO): LEO will be a method that attempts to improve on MAML using evolutionary algorithms. It attempts to improve MAML performance by evolutionarily determining the appropriate learning rate and initialization for new tasks.
• ANIL (Almost No Inner Loop): ANIL is a method that reduces computational cost by eliminating the inner loop of MAML (task-specific fine-tuning); ANIL takes the approach of adapting model initialization parameters to new tasks without fine-tuning.
• Meta-SGD: Meta-SGD is a meta-learning approach in which the model update algorithm itself is meta-learned. By updating the initialization parameters using the meta-learned optimization algorithm, the performance of MAML is improved.
• Multi-Task MAML (MT-MAML): MT-MAML is a meta-learning method that considers multiple tasks simultaneously and uses the characteristics of each task. It aims to take advantage of commonalities between tasks to adapt the model more effectively.

Memory-Augmented Networks

Memory-Augmented Networks (MANs) are a form of meta-learning, a technique that stores experience from past tasks and uses that information when applying it to new tasks. This approach allows for easy adaptation to new tasks while retaining past knowledge and experience.

The following is an overview of a typical method using a memory expansion network.

1. Memory Construction:
   • During the meta-training phase, training is performed on several basic tasks, and the data, model parameters, and gradient information for each task are stored in a memory.
   • This memory is a record of experience in previous tasks and is used during adaptation to new tasks.
2. Memory Utilization:
   • During the meta-testing phase, the information in memory is used to perform the appropriate initialization for the new task.
   • Knowledge from previous tasks is retrieved from memory and used to set initial model parameters for the new task and to adjust the model architecture.
3. Learning and Adaptation:
   • Data from the new task is used to train and adapt the model. Information from memory is used to guide the model in the appropriate direction during initialization and updating.
   • Information from memory helps achieve adequate performance for new tasks by influencing the model’s learning process and initialization.

An advantage of the memory expansion network will be the ability to effectively reuse experience from previous tasks. This increases the likelihood of achieving good performance with less data in new tasks.

Examples of memory extension networks include the Neural Turing Machine (NTM) and Differentiable Neural Computer (DNC) architectures that have been proposed and studied for meta-learning.

Neural Turing Machine（NTM）

Neural Turing Machine (NTM) is a memory-based network architecture proposed in the field of machine learning and artificial intelligence. NTM is an attempt to combine memory and computational capabilities by incorporating read/write operations to external memory into a regular neural network, and is expected to show better performance for complex tasks and sequential processing than conventional neural networks.

The main features and architecture of NTMs are described below.

• External memory: NTMs have external memory cell banks as well as internal neural network units. This allows the model to read and write data, enabling information to be retained and retrieved.
• Read/Write Heads: NTMs have heads to perform read/write operations. These heads are used to read or write to specific locations in memory. The heads are operated based on instructions from the neural network controller.
• Controller: The controller of an NTM is a regular neural network unit that controls the read/write heads and handles computational tasks. The controller learns how to access external memory.
• Attention mechanism: The NTM uses an attention mechanism, also described in “Attention in Deep Learning” to select where to read and write. The attention mechanism extracts important information in memory based on a given query or context.

NTMs may be applied to a variety of tasks because of their capabilities. This could be, for example, sequential data or program execution, or modeling of long-term dependencies. However, NTMs themselves have their own challenges, including difficulties in design and training, and increased computational costs.

Since the proposal of NTM, various derived models and improved architectures have been studied and memory-based approaches have been applied to various tasks. Some of the derived models and improved architectures are described below.

• Differentiable Neural Computer (DNC): DNC is an extension of NTM that introduces differentiable mechanisms for read/write head behavior and memory access in addition to the memory-based architecture. This increases the likelihood that DNC will be more effectively learned and applied to a wide variety of tasks.
• Memory Networks: Memory Networks will be a network architecture with memory for information retention and retrieval; unlike NTMs, it will feature a memory component separate from the regular neural network and will use the Attention Memory reads and writes are controlled using the Attention mechanism.
• Transformer: Transformer described in “Overview of Transformer Models, Algorithms, and Examples of Implementations“ is a neural network architecture centered around the Attention Mechanism that has been very successful for tasks such as natural language processing.Transformer is based on the idea of NTM and uses self-attention to process sequential data and model long-term dependencies.
• Hybrid Models: Hybrid models combining the NTM architecture with other network architectures have also been proposed. For example, models combining CNN described in “Overview of CNN and examples of algorithms and implementations” or RNN as described in “Overview of RNN and examples of algorithms and implementations” with NTM have been applied to image processing and natural language processing tasks.
• Meta-Learning with Memory Augmentation: Another approach combines meta-learning with memory-based architectures. This may allow models to adapt quickly to new tasks.

Differentiable Neural Computer（DNC）

Differentiable Neural Computer (DNC) will be an enhanced memory-based neural network architecture with external memory and read/write head DNC will be trainable using differentiable mechanisms while having the ability to store and compute and the architecture is said to be well suited for modeling complex sequential patterns of tasks and long-term dependencies.

The main features and elements of DNC are described below.

• Memory Matrix: The DNC has an external memory matrix (Memory Matrix). This memory matrix is accessed by the read/write head and used to store data. Each cell in the memory matrix contains a vector or vector sequence.
• Read/Write Heads: A DNC has multiple read/write heads, each of which performs a read/write operation on a specific location in the memory matrix. The read/write heads are controlled by a position attaching mechanism and access the appropriate location in the memory matrix for reading or writing.
• Controller: The controller of a DNC is an ordinary neural network that controls the operation of the read/write heads and performs task-specific computations. The controller learns where to read and write information and how to access it.
• Attention Mechanism: DNCs use the Attention Mechanism to control the weights with which the read/write heads access specific locations in the memory matrix. This enables focus on critical information and the capture of long-term dependencies.

DNC is applied to sequential task and program execution, multi-step inference, and modeling of long-term dependencies, etc. DNC, due to its memory-based structure and differentiability, overcomes the limitations of neural networks and has the potential for superior performance in complex tasks. DNC is a method that has the potential to overcome the limitations of neural networks and provide superior performance in complex tasks.

The idea of Differentiable Neural Computer (DNC) provides a powerful architecture that combines memory and computation, and various derivative models and improved architectures have been proposed since then. Some of the derived models and improved architectures are described below.

• Differentiable Unordered Memory Access (D-UMA): D-UMA is an approach to improve the control mechanism of read/write heads of DNC. While normal DNC read/write heads perform sequential, ordered accesses, D-UMA enables unordered memory accesses and is suitable for processing unordered data.
• Neural Random-Access Machines (NRAM): NRAM is an architecture that has external memory but allows random access during memory addressing; NRAM is effective for access patterns that do not rely on sequentiality.
• Hybrid DNC Models: Models that combine DNC with other network architectures have been proposed. For example, DNCs are being combined with recurrent neural networks (RNNs) to model long-term sequential dependencies.
• Addressable Content-Based Memory Networks: Based on the DNC architecture, addressable content-based memory networks have been proposed. This improves memory access and enables fast information retrieval.
• Graph DNC (GDNC): GDNC will be an architecture for applying DNC to graph data processing. It stores node and edge information in external memory and allows manipulation and inference of graph data.

These derived models and improved architectures are being studied in an attempt to extend the idea of DNC in various aspects and to apply it to a wider range of tasks; not only is DNC itself an important example of a memory-based architecture, but its extended versions and applied models also illustrate the evolution of methods to combine memory and computation It is an example of the evolution of the methodology.

ProtoNets（Prototypical Networks）

ProtoNets (Prototypical Networks) is a network architecture proposed as part of a meta-learning approach, a type of Few-Shot Learning (learning with less data). ProtoNets is a network architecture that provides a new class with fewer support set (e.g., one or a few labeled data), it aims to generate prototypes to effectively identify instances of the class, even when given a support set (e.g., one or a few labeled data).

The main idea of ProtoNets is to represent instances of each class as prototypes in the feature space, which allows for an approach to identifying unknown data in which the distance between the data and the prototype of each class is calculated and classified into the closest class.

The procedure for ProtoNets is as follows

• Support set prototype computation: using the data in the support set for each class, the prototype for that class is computed in the feature space. This can be done by simply averaging the feature vectors.
• Calculate the distance between the data and the prototype: Map the unknown data into the feature space and calculate the distance to the prototype of each class. Typically, Euclidean distance is used.
• Classification: Classify the data into classes whose prototypes have the closest distance.

ProtoNets has been reported to perform well in training with small amounts of data and in discriminating against unknown classes. In addition, ProtoNets is very widely used in the research field of Few-Shot Learning because of its simple architecture and relative ease of implementation.

However, ProtoNets also have general limitations, and challenges can exist, especially with large data sets and high-dimensional feature spaces. Nevertheless, ProtoNets is positioned as one important baseline for Few-Shot Learning.

Due to its simple architecture and effective performance, various derivatives and improved architectures of ProtoNets have been proposed. These are described below.

• Relation Network (RelationNet): RelationNet is an extension of ProtoNets, an architecture that learns relationships between pairs of data. class relationship to perform classification. This allows modeling interactions between data and has been reported to achieve higher performance than ProtoNets.
• Matching Networks: Matching Networks will be a method for classifying unknown data by using an attention mechanism that assigns weights to each data in the support set. The data in the support set is weighted to compute a prototype in the feature space, and the classification is performed by computing the similarity to the unknown data, which allows for better consideration of the importance of each data.
• Memory-Augmented Neural Networks: several models have attempted to extend ProtoNets by introducing memory-based approaches. Memory cells or external memory may be used to hold prototypes for each class, which may then be used for comparison with unknown data.
• Hierarchical Prototypical Networks: Hierarchical Prototypical Networks are proposed by considering the hierarchical structure of data. This allows for more precise modeling of the relationships between classes of data and improved performance when training with less data.
• Domain-Adaptive Prototypical Networks: An improved version that considers domain adaptation also exists. This attempts to improve classification performance in unknown domains using support sets from different domains.

These derived models and improved architectures further extend the idea of ProtoNets to derive optimal performance for specific tasks and challenges.

Reptile

Reptile is a type of meta-learning (learning from learning), which is an approach for adapting a model to multiple tasks. Specifically, it provides an approach for fine-tuning a model with a support set (small amount of training data) for one task and then adapting that model to other tasks. the name Reptile comes from “iteratively learning and adapting like a reptile (reptile)”.

The main ideas behind Reptile are as follows

• Initialization and fine-tuning: Reptile first randomly initializes the model and then fine-tunes (learns) it within a fixed iteration on a support set for each task. This produces an initialized model for each task.
• Meta-gradient calculation: Based on the results of the fine-tuning performed within an iteration, the meta-gradient of each parameter (the average or sum of the fine-tuning results for each task) is calculated. This ensures that the learning for each task is reflected in the meta-gradient of the model as a whole.
• Meta-update: The computed meta-gradients are used to update the initialized model. This produces a model that integrates information from multiple tasks.

Reptile has the following characteristics

• Simplicity and efficiency: Reptile is a simple approach, and the learning process is efficient because task adaptation is done through an iterative process of initialization and fine-tuning.
• Generality of task adaptation: Reptile is generally applicable to multiple tasks and can be used for multiple variations of the same task and for adaptation across different tasks.
• Generalization of learning rates: Reptile does not require learning rates to be adjusted to a specific task, and task adaptation is achieved through a combination of initialization and fine-tuning.

Reptile will be a method of interest in the field of meta-learning research due to its concise concept and effective performance. However, it has limitations in modeling adaptability and complex relationships among tasks, and other meta-learning methods are worth considering depending on the characteristics of the task and data.

Because of the simplicity and extensibility of the Reptile idea, various derivative models and improved architectures have been proposed. These derived models and improved architectures aim to improve Reptile’s performance or adapt it to specific tasks or domains. They are described below.

• Multi-Step Reptile: While normal Reptile performs fine-tuning within a fixed iteration for each task, Multi-Step Reptile performs fine-tuning for multiple tasks sequentially. It is said that a more adaptive model can be obtained by performing fine-tuning in multiple iterations for each task.
• Adaptive Reptile: Adaptive Reptile will be an approach that applies a different learning rate for each task. The goal is to adjust the learning rate according to the characteristics of each task to achieve effective fine-tuning.
• High-Dimensional Reptile: High-Dimensional Reptile is an improved version of Reptile that applies Reptile when the feature space is high-dimensional. It proposes a method for Reptile to work well in high-dimensional data and feature spaces.
• FOMAML (First-Order MAML): FOMAML is a method that applies the idea of Reptile to meta-gradient computation. It improves computational efficiency by using only first-order derivatives instead of second-order derivatives when computing meta-gradients.
• Regularized Reptile: There is also an attempt to improve the generalization performance of the model by introducing regularization. It is believed that this can produce models that can be applied to multiple tasks while suppressing over-learning.
• Domain-Adaptive Reptile: There are also approaches that extend Reptile by considering domain adaptation. Methods have been proposed to increase adaptability across multiple domains.

These derived models and improved architectures build on the basic ideas of Reptile, but with improvements focused on task and data characteristics, learning efficiency, and performance improvement. Since individual approaches exhibit different results depending on their characteristics, it is important to select the best method for a particular task.

Implementation Procedure for Meta-Learners

The general procedure for implementing Meta-Learners is as follows. Prototypical Networks is used here as an example.

• Data Preparation:
  • Prepare a support set and a query set. The support set consists of a small number of samples of each class, and the query set consists of unknown data.
• Design the network architecture:
  • Select the network architecture for meta-learning, typically a convolutional neural network (CNN) for Prototypical Networks.
• Configure the learning process:
  • Set up the learning process for meta-learning, which for Prototypical Networks includes the steps of calculating a prototype for each class using the data for each class in the support set and calculating the distance between the data in the query set and the prototypes.
• Meta-training:
  • Meta-training is performed on multiple tasks within the meta-learning framework. Typically, multiple episodes (support set and query set pairs) are prepared for each task and the network parameters are updated.
• Metatest:
  • After meta-training is completed, we evaluate whether the network parameters have acquired the ability to adapt to the task. This is done using the new task support set and query set.
• Task Adaptation:
  • If the meta-test is successful, the network can quickly adapt to the new task. During task adaptation, the network is fine-tuned using the data in the support set.
• Result Evaluation:
  • To evaluate the success of meta-learning, evaluate the performance during task adaptation. The goal is to achieve high performance for unknown tasks.

Libraries and platforms available for Meta-Learners

A variety of libraries and platforms can be utilized when implementing Meta-Learners. Some common libraries and platforms are described below.

• PyTorch: PyTorch is a deep learning framework and is widely used for implementing meta-learning; using PyTorch allows for flexible implementation of custom model architectures and learning algorithms.
• TensorFlow: TensorFlow is another widely used deep learning framework that provides meta-learning related algorithms and functions that can be leveraged and implemented.
• scikit-learn: scikit-learn is a Python-based machine learning library that can be used to implement Few-Shot Learning and Meta-Learners. In particular, it provides functions for classifying classes and evaluating model adaptability.
• Higher: Higher is a PyTorch extension library for meta-learning, which facilitates the computation of higher-order derivatives (metagradients) and the implementation of meta-learning algorithms.
• learn2learn: learn2learn is a library for meta-learning and is based on PyTorch. It provides different meta-learning algorithms and datasets to facilitate experimentation and evaluation.
• Meta-RL Platforms: Meta-World and ML^3, which are platforms for meta-reinforcement learning, are used to support evaluation and experimentation of meta-learning algorithms.

Meta-Learners application examples

The following are examples of Meta-Learners applications.

• Few-Shot Learning: Meta-Learning is particularly useful for learning with little data. Even when there is only a small amount of labeled data for a new class, the meta-learning algorithm can be used to quickly adapt the model.
• One-Shot Learning: One-shot learning is the problem of identifying a class when only one example is given. Meta-learning can be an effective method to train a model using a single example as a support set and to perform classification on a query set.
• Zero-Shot Learning: In Zero-Shot Learning, classification is performed using a learned model for an unknown class. Meta-learning can be used to increase task adaptability even for unknown classes.
• Transfer Learning: meta-learning is also used to help transfer knowledge between different tasks or domains. It is applied as a method to improve adaptability to new tasks based on learned models.
• Domain Adaptation: Domain adaptation requires adapting models to data from different domains. A method is proposed that uses meta-learning to improve adaptability to new domains.
• Incremental Learning: Meta-learning is used in situations where new classes are added sequentially. It provides a set of supports for new classes to adapt the model and improve learning efficiency.
• Active Learning: Metalearning is used to effectively select unknown data when training a model with less labeled data.

Example implementation of Few-Shot Learning with Meta-Learners

The following is a simple PyTorch-based implementation of Few-Shot Learning using Prototypical Networks.

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# Data set preparation (temporary data)
class FewShotDataset(torch.utils.data.Dataset):
    def __init__(self, num_classes, num_samples_per_class, num_query_samples):
        self.num_classes = num_classes
        self.num_samples_per_class = num_samples_per_class
        self.num_query_samples = num_query_samples
        self.data = torch.randn(num_classes, num_samples_per_class + num_query_samples, 128)  # 128次元の特徴ベクトル

    def __len__(self):
        return self.num_classes

    def __getitem__(self, idx):
        support_set = self.data[idx, :self.num_samples_per_class]
        query_set = self.data[idx, self.num_samples_per_class:]
        return support_set, query_set

# Model Definition for Prototypical Networks
class PrototypicalNetworks(nn.Module):
    def __init__(self):
        super(PrototypicalNetworks, self).__init__()

    def forward(self, support_set, query_set):
        # Calculate support set prototypes
        support_set_prototype = torch.mean(support_set, dim=0)

        # Calculate distance between query set and prototype
        distances = torch.norm(support_set_prototype - query_set, dim=1)

        return distances

# hyperparameter
num_classes = 5
num_samples_per_class = 5
num_query_samples = 5
learning_rate = 0.001
num_epochs = 10

# Preparing Data Sets and Data Loaders
dataset = FewShotDataset(num_classes, num_samples_per_class, num_query_samples)
dataloader = DataLoader(dataset, batch_size=1, shuffle=True)

# Model and Optimizer Preparation
model = PrototypicalNetworks()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# learning loop
for epoch in range(num_epochs):
    for support_set, query_set in dataloader:
        optimizer.zero_grad()
        distances = model(support_set.squeeze(), query_set.squeeze())
        
        # Calculate cross-entropy loss and back-propagate
        loss = torch.mean(distances)
        loss.backward()
        optimizer.step()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")

print("Training finished.")

This code is a simple example of Few-Shot Learning using Prototypical Networks.

Example implementation of One-Shot Learning with Meta-Learners

One-Shot Learning is the problem of identifying classes when only one sample is available. The following is a simple PyTorch-based implementation of One-Shot Learning.

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# Data set preparation (temporary data)　
class OneShotDataset(torch.utils.data.Dataset):
    def __init__(self, num_classes):
        self.num_classes = num_classes
        self.data = torch.randn(num_classes, 1, 128)  # 128-dimensional feature vector
　　　　　　　　　
    def __len__(self):
        return self.num_classes

    def __getitem__(self, idx):
        return self.data[idx]

# Model Definition for One-Shot Learning
class OneShotModel(nn.Module):
    def __init__(self):
        super(OneShotModel, self).__init__()
        self.fc = nn.Linear(128, num_classes)

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

# hyperparameter
num_classes = 10
learning_rate = 0.001
num_epochs = 10

# Preparing Data Sets and Data Loaders
dataset = OneShotDataset(num_classes)
dataloader = DataLoader(dataset, batch_size=1, shuffle=True)

# Model and Optimizer Preparation
model = OneShotModel()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()

# learning loop
for epoch in range(num_epochs):
    for data in dataloader:
        optimizer.zero_grad()
        inputs = data.squeeze()  # Change size (1, 128) to (128,)
        outputs = model(inputs)
        labels = torch.tensor([inputs.argmax().item()])  # Dummy labels
        loss = criterion(outputs.unsqueeze(0), labels)  # Calculation of cross-entropy loss
        loss.backward()
        optimizer.step()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")

print("Training finished.")

# Evaluation of One-Shot Learning
with torch.no_grad():
    new_data = torch.randn(1, 128)  # Feature vector of unknown data
    outputs = model(new_data)
    predicted_class = torch.argmax(outputs).item()

print(f"Predicted class for new data: {predicted_class}")

Example implementation of zero-shot learning with Meta-Learners

Zero-shot learning is the task of performing classification on an unknown class using a learned model, and a simple PyTorch-based implementation for zero-shot learning is shown below.

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# Data set preparation (temporary data)
class ZeroShotDataset(torch.utils.data.Dataset):
    def __init__(self, num_classes):
        self.num_classes = num_classes
        self.data = torch.randn(num_classes, 128)  # 128-dimensional feature vector

    def __len__(self):
        return self.num_classes

    def __getitem__(self, idx):
        return self.data[idx]

# Model Definition for Zero-Shot Learning
class ZeroShotModel(nn.Module):
    def __init__(self):
        super(ZeroShotModel, self).__init__()
        self.fc = nn.Linear(128, num_classes)

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

# hyperparameter
num_classes = 10
learning_rate = 0.001
num_epochs = 10

# Preparing Data Sets and Data Loaders
dataset = ZeroShotDataset(num_classes)
dataloader = DataLoader(dataset, batch_size=1, shuffle=True)

# Model and Optimizer Preparation
model = ZeroShotModel()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()

# learning loop
for epoch in range(num_epochs):
    for data in dataloader:
        optimizer.zero_grad()
        inputs = data
        outputs = model(inputs)
        labels = torch.tensor([inputs.item()])  # Dummy labels
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")

print("Training finished.")

# Evaluation of Zero-Shot Learning
with torch.no_grad():
    new_class_data = torch.randn(1, 128)  # Unknown class feature vector
    outputs = model(new_class_data)
    predicted_class = torch.argmax(outputs).item()

print(f"Predicted class for new data: {predicted_class}")

Reference Information and Reference Books

See also “Small Data Learning, Combining Logic and Machine Learning, and Local/Group Learning” which discusses related approaches.

For reference books, see “Small Data Analysis and Machine Learning.

“Data Analytics: A Small Data Approach”