One-Shot Learning: Learn How to Build Models with Limited Labeled Data

One-Shot Learning: Learn How to Build Models with Limited Labeled Data

One of the biggest challenges in machine learning is the need for large amounts of labeled data to train models effectively. However, in many real-world scenarios, obtaining labeled data can be difficult, time-consuming, or expensive. This is where one-shot learning comes in - a technique that enables machines to learn and generalize from a single example.

One-shot learning is a type of machine learning that falls under the category of few-shot learning. This means that instead of having access to a large dataset with many examples for each class, one-shot learning models are trained on a small set of labeled examples per class.

The goal of one-shot learning is to teach the machine to learn from a few examples so that it can accurately classify new, unseen examples with a high degree of accuracy. This is a critical capability in situations where acquiring large amounts of labeled data is impractical or impossible.

One-Shot Learning: Learn How to Build Models with Limited Labeled Data

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In this article, we will delve into the world of one-shot learning and provide a comprehensive guide to mastering this technique. We will cover topics such as

  • Siamese networks, 
  • Metric learning, 
  • Prototype-based learning.

Which are all crucial components of one-shot learning.

We will also discuss the challenges and limitations of one-shot learning and explore various strategies to overcome these limitations. Finally, we will provide a detailed overview of evaluation metrics used to assess the performance of one-shot learning models.

By the end of this article, you will have a solid understanding of one-shot learning and be equipped with the tools to build and train your own one-shot learning models with limited labeled data.

What is One-Shot Learning?

One-Shot Learning, as the name subtly implies, is about learning from one, or just a few, examples. Imagine meeting a person and remembering their face after just one encounter - that’s essentially how one-shot learning works in the realm of machines. 

This approach teaches models to recognize and make accurate predictions from a very limited set of data, which is in stark contrast to conventional methods that often require massive datasets to function effectively.

Consider meeting a new colleague, Alex, for the first time in a large company. The next day, you recognize Alex among all other faces in the cafeteria, despite only seeing them once. Your brain, in this scenario, has effectively employed a kind of one-shot learning, managing to identify and remember Alex with just a single example.

In a technical context, one-shot learning can be seen as a classification problem where the model is required to make predictions after being exposed to only one or very few training examples per class. The sheer challenge here is how to develop a model that can generalize well from such a sparse dataset.

A commonly used technique in one-shot learning is employing Siamese Neural Networks, which excel at understanding the similarity between pairs of inputs. Siamese Networks involve two identical subnetworks, each taking one of the two input instances, and the outputs are merged to predict whether the instances belong to the same class or not.

One-Shot Learning Technical Example

In facial recognition technology, let’s consider the case where a security system must identify an individual (say, a new employee) based on their ID card. Traditional models might struggle due to the lack of extensive training data for this individual. 

However, with one-shot learning, the model compares the new image with the single available ID card image, evaluating their similarity, and thus, accurately recognizing the individual even in the absence of a rich dataset.

In a deeper algorithmic context, the functioning of Siamese Neural Networks in one-shot learning could be exemplified as follows:

  • Twin Networks: Two identical neural networks are trained to extract features of two inputs and understand the similarities/dissimilarities between them.

  • Contrastive Loss: This is commonly used to measure how well the network distinguishes between the pairs of inputs. The objective during training is to minimize the distance between similar pairs and maximize the distance between dissimilar pairs.

  • Embedding Learning: The networks transform input data into a rich embedding space where similar items are pulled together and dissimilar items are pushed apart.

One-Shot Learning Techniques

One-shot learning is a technique that allows a model to learn from very few labeled examples. Here are some commonly used one-shot learning techniques:

Siamese networks

Siamese networks are a type of neural network that is trained to recognize similarity between pairs of inputs. They are commonly used for one-shot learning tasks, such as facial recognition, where there are very few labeled examples of each individual. 

Siamese networks consist of two identical sub-networks that share the same weights, and the output of the network is a similarity score between the two inputs.

Example code for building a simple Siamese network in Keras

Metric learning

Metric learning is a type of machine learning that focuses on learning a distance metric between data points. In one-shot learning, metric learning is used to learn a similarity measure between examples, allowing the model to generalize to new examples with very few labeled data points.

Example code for using the metric learning algorithm, triplet loss, to train a model for one-shot learning

Prototype-based learning

Prototype-based learning is a one-shot learning technique that aims to identify the class of an unseen data point by comparing it to a set of prototypes or representatives of each class. In this approach, the model learns to represent each class by a single prototype, which can be thought of as a representative point in the feature space that captures the essential characteristics of the class.

The prototype-based approach is based on the idea that classes can be represented by a set of examples that are close to each other in the feature space. Given a new data point, the model compares it to the prototypes of each class and assigns it to the class whose prototype is closest to it.

Example code to build Prototype based learningusing NearestCentroid

Step by Step How One-Shot Learning Works

Let's carve out a pathway through the internal workings of one-shot learning, lending insights into how it manages to conjure precise model predictions with scarcely limited data.

One-shot learning diverges from traditional machine learning by mastering the art of deriving accurate predictions with minimal data - typically, just one or a very few examples per class. 

This scarcity of data propels one-shot learning models to harness the potential of their algorithms, capitalizing on nuanced learning from available examples and thus, transcending the boundaries of conventional learning models.

Step 1: The Anatomy of Limited Data Learning

  • Grappling with Scarcity: The core challenge revolves around training a model sufficiently using limited instances.
  • Defining the Essence: Identify and select data that is maximally representative of each class or category.
  • Strategic Data Preprocessing: Engage in meticulous data preprocessing, ensuring that the scarce data is fully optimized for model training.

Step 2: Navigating Through Model Selection and Architecture

  • Opting for Siamese Networks: Siamese Networks, among other architectures, cater to the demands of one-shot learning, given their proficiency in identifying and learning from data disparities and similarities.
  • Embedding Learning: The model must deftly navigate through feature extraction, learning to differentiate between classes from meager examples.

Step 3: The Ingenuity of Training Strategies

  • Delving into Embedding: Develop a metric space using embedding learning, where input instances are mapped such that similar instances converge and dissimilar ones diverge.
  • Augmenting Data: Implement data augmentation strategies to synthetically amplify data availability, thereby aiding in robust model training.

Step 4: The Craft of Similarity Learning and Classification

  • Metric Learning: Establish a metric or similarity measure in the embedded space, quantifying the similarities or disparities between instances.
  • Classifier Design: Build a classifier that leverages embedded features to make accurate classifications or predictions.

Step 5: The Rigorous Evaluation of Model Performance

  • Utilizing Evaluation Metrics: Implement relevant metrics, such as accuracy or F1 Score, to scrutinize the model's predictive performance.
  • Navigating Overfitting: With limited data, ensuring the model doesn’t overfit and retains generalization capabilities becomes pivotal.

Step 6: Model Deployment and Real-world Prediction

  • Deploying Models: Integrate the model into the targeted application, ensuring seamless functionality.
  • Undertaking Predictions: The model, trained on limited instances, makes predictions on unseen data, corroborating its learned embeddings and classification boundaries.

Example Insight: One-Shot Learning in Character Recognition

Imagine a scenario where a model is required to recognize handwritten characters of a rarely-used ancient script, with only one example per character available.

  • Utilizing One Example per Character: The model is presented with a single instance per character, which it must learn to recognize meticulously.
  • Siamese Network Application: A Siamese Network is deployed, learning to differentiate between different characters from the minimal examples.
  • Data Augmentation: To circumvent the data limitation, augmentation strategies like rotating, scaling, and noise addition are employed to artificially expand the training data.
  • Real-world Recognition: Once trained, the model identifies characters in real-world instances of the script, validating its one-shot learning capability.

In a nutshell, one-shot learning orchestrates a methodology that meticulously learns and predicts from minimal data, bridging the gap between scarce data and accurate predictions, and finding its place across various applications such as facial recognition, character recognition, and more, where data is a prized and limited asset.

Limitations of One-Shot Learning

One-shot learning has several advantages, such as the ability to learn new classes with very few examples and the ability to generalize to unseen examples. However, it also faces several limitations that can affect its performance in practice.


One of the main problems with spot training is the risk of overtraining. When working with very limited model data, it is easy to adjust for noise in the data rather than the underlying model. This can lead to poor generalization performance if the model fails to classify new examples correctly. 

To overcome this problem, regularization techniques such as L1 regularization, L2 regularization and early stopping can be used to avoid model overfitting.

Small sample sizes

Another one-off learning problem is that of small sample sizes. With only a few examples of each class, it can be difficult for the model to learn a good representation of the underlying model. This can degrade performance and accuracy.

To overcome this problem, techniques such as data augmentation, transfer training, and domain customization can be used to leverage additional data and improve model performance.

Need for high-quality feature representations

Finally, one-shot learning requires high-quality feature representations that capture the essential characteristics of the data. If the features are not well-defined or are not representative of the underlying patterns, the model may fail to learn a good representation and may perform poorly. 

To overcome this challenge, it is important to carefully design the feature representation and to preprocess the data to remove noise and irrelevant information.

How to Overcome One-Shot Learning Limitations

A strategy was developed to overcome the limitations of point training to improve the model's generalization ability and reduce the risk of overfitting. These strategies include data augmentation, transfer learning, and ensemble learning.

Data augmentation

Data augmentation is a technique that involves generating additional training examples by applying various transformations to the existing data. By creating new examples with different rotations, translations, and other modifications, we can increase the diversity of the training data and help the model learn more robust representations.

Here's some example code for data augmentation in Python using the Keras library:

This code defines an ImageDataGenerator object that applies various transformations to the training data images. Then use the flow_from_directory method to load the data and apply real-time padding during training.

Transfer learning

Transfer learning is a technique that involves leveraging a pre-trained model to learn a new task with limited data. By using a model that has already been trained on a large dataset, we can benefit from the knowledge and feature representations that it has already learned.

Here's some  code for transfer learning using the VGG16 model in Keras:

In this example, we load the pre-trained VGG16 model and freeze its layers so that we can use it as a feature extractor. We then add a new classification layer on top and create a new model that can be trained on our limited data.

By leveraging the pre-trained features, we can learn a good representation of the data with very few examples.

Ensemble learning

Ensemble learning is a technique that combines multiple models to improve performance. By training multiple models with different hyperparameters, architectures, or training data, we can create an ensemble that can learn a more robust representation of the data.

Here is an example of how ensemble learning can be implemented using Python

This example defines three separate models. A K nearest neighbor (KNN) classifier, a decision tree classifier, and a logistic regression classifier. We then use scikit-learn's VotingClassifier to define an ensemble model that combines the predictions of these three models using a strict voting scheme. 

Finally, fit the ensemble model to the training data and make predictions on the test data. This technique can be used to improve the performance of point-learning models by combining the strengths of several models.

Evaluation Metrics for One-Shot Learning

To judge the performance of machine learning models, you need metrics. For one-shot learning, evaluating model performance becomes even more important when the number of labeled samples is limited. Here are three commonly used metrics for one-shot learning:


Accuracy is the most common evaluation metric used in machine learning. It is defined as the number of correctly classified samples divided by the total number of samples. 

In one-shot learning, the accuracy metric measures how well the model can correctly classify a new image based on a single sample

Precision and recall

Precision and recall are two complementary evaluation metrics that are commonly used in binary classification problems. 

Precision measures how many of the samples that the model classified as positive are actually positive. Recall measures how many of the actual positive samples were correctly identified by the model.

In one-shot learning, precision and recall can be used to evaluate the model's ability to correctly identify new samples based on a single example.

F1 score

The F1 score is the harmonic mean of precision and recall. Provides a single score that balances accuracy and completeness. For one-shot learning, the F1 score can be used as a summary metric to assess the overall performance of the model.

To calculate these evaluation metrics in Python, we can use scikit-learn library's metrics module. 

Here's an example of calculating accuracy, precision, recall, and F1 score for a one-shot learning model

In this example, we use the predict method of the one-shot learning model to make predictions on the test set. We then calculate the accuracy, precision, recall, and F1 score using the corresponding functions from the scikit-learn metrics module. Finally, we print the values of each metric for evaluation.

Similar Types to One-Shot Learning: A Glimpse into Low-Data Learning Strategies

One-shot learning has garnered attention in the machine learning community for its unique ability to perform accurate predictions with minimal data. But it is not alone in the quest to produce effective models when data is scarce.

Let’s delve into other paradigms which, much like one-shot learning, strive to leverage limited data in machine learning.

Few-Shot Learning

Few-shot learning, as the name implies, involves training models using a very small dataset. While one-shot learning utilizes a single instance, few-shot learning may use a handful (such as five or ten examples per class).

It employs techniques like data augmentation, meta-learning, and transfer learning to maximize the utility of available data. It’s widely used in image classification, object detection, and natural language processing where obtaining a large dataset is challenging or impractical.

Striking a balance between avoiding overfitting and achieving adequate model training with such limited data remains a critical challenge. This is addressed by leveraging pre-trained models, advanced data augmentation, or generating synthetic data.

Zero-Shot Learning

Zero-shot learning operates without any initial examples of the class it is intended to recognize or classify. Instead, it leverages side information or attributes associated with each class to make predictions.

Utilizing semantic relationships, attribute-based recognition, and utilizing knowledge graphs, zero-shot learning finds applications in areas like text categorization, object recognition, and more, where it's impractical to have labeled instances of all classes.

Ensuring the model generalizes effectively from seen to unseen classes and accurately utilizes semantic or attribute information is pivotal. Solutions often involve developing sophisticated attribute representations and utilizing transfer learning strategies.

Transfer Learning

Transfer learning involves training a model on a larger, related dataset, and then transferring that learned knowledge to a smaller dataset. It doesn’t operate on limited data per se, but it is a strategy to boost learning where data is scarce.

It involves pre-training and fine-tuning phases and is prominently used across various domains like computer vision, speech recognition, and natural language processing, helping models generalize well even with limited target data.

Selecting a relevant source dataset and avoiding negative transfer (where transferred knowledge adversely impacts performance) are key challenges. Ensuring domain similarity and employing domain adaptation techniques provide possible solutions.

Semi-Supervised Learning

Semi-supervised learning combines a small amount of labeled data with a larger pool of unlabeled data during training, utilizing both to enhance learning outcomes.

By using strategies like self-training, multi-view training, and co-training, semi-supervised learning finds its application in areas like image classification, bioinformatics, and speech analysis, where labeled data may be costly to obtain.

Ensuring the unlabeled data assists rather than hinders learning is vital. Strategies often involve iterative labeling of unlabeled data, utilizing confidence thresholds to gradually expand the labeled dataset.

Self-Taught Learning

Self-taught learning is a paradigm where the model is initially trained with abundant, unlabeled data before utilizing a smaller labeled dataset for fine-tuning.

With techniques like autoencoders for feature learning and subsequent supervised fine-tuning, self-taught learning is utilized in applications such as image recognition and natural language processing.

Effective feature learning from unlabeled data and ensuring subsequent fine-tuning effectively leverages these features are crucial aspects. This involves sophisticated unsupervised learning techniques and careful integration of supervised learning stages.

One-Shot Learning Implementation in Python 

Let's use the breast cancer dataset from Sklearn, which contains features computed from digitized images of a fine needle aspirate (FNA) of a breast mass. The goal is to predict whether the mass is malignant or benign.

We will use a simple one-shot learning approach called k-nearest neighbors (KNN) and evaluate its performance using accuracy and F1 score. Then, we will use data augmentation to overcome the limitation of small sample size and compare the results.

Here’s the code to load the data first and plot the class

One-shot learning data visuavalization

In the above code we loaded the dataset and split it into training and testing sets.

Next, we used KNN to perform one-shot learning on the dataset. KNN is a simple machine learning algorithm that works by finding the k nearest neighbors in the training set to the test point and predicting the majority class of those neighbors.

Next we evaluate the performance of the KNN classifier using accuracy and F1 score:


  • Accuracy: 0.956140350877193
  • F1 score: 0.9659863945578232

The KNN classifier achieved an accuracy of 0.956 and an F1 score of 0.965 on the test set. However, as we mentioned earlier, one of the main challenges of one-shot learning is small sample size. 

We only have 569 samples in this dataset, which is not a lot for a machine learning model to learn from. To overcome this limitation, we can use data augmentation to generate new samples from the existing data.

Now, let's apply data augmentation to overcome the limitation of small sample sizes in one-shot learning. We will use random oversampling to balance the class distribution of the dataset.

we trained the model on augmented data and then print the metrics to see if it worked.


  • Accuracy: 0.9736842105263158
  • F1 score: 0.9793103448275862

This code first separates the majority and minority classes in the training data, then uses random oversampling to create a new set of examples for the minority class that matches the size of the majority class. 

The majority class and upsampled minority class are then combined to create a new balanced dataset. Finally, the KNN model is trained on the new balanced dataset, and predictions are made on the test data to calculate the accuracy score.

From here we can see that using data augmentation definitely helped the one-shot learning and it helped to increase the metrics.


In conclusion, one-shot learning is a powerful technique for training machine learning models with limited labeled data. 

By learning to recognize new classes with just one or a few examples, one-shot learning has the potential to reduce the need for extensive labeled data in many real-world applications. While there are still some limitations and challenges associated with one-shot learning, researchers are actively working on developing new techniques to overcome them.

As machine learning continues to evolve, it is likely that one-shot learning will play an increasingly important role in enabling more efficient and effective learning with limited labeled data.

Frequently Asked Questions (FAQs) On One-Shot Learning

1. What is One-Shot Learning?

One-Shot Learning refers to training machine learning models using a very small dataset, often just one or a few examples per class, to make accurate predictions.

2. How is One-Shot Learning different from traditional machine learning?

Unlike traditional learning that requires large amounts of data to train models, One-Shot Learning enables model training with limited labeled data by focusing on learning similarities or distinguishing features.

3. When is One-Shot Learning applicable?

One-Shot Learning is particularly applicable in scenarios where obtaining large labeled datasets is impractical, such as rare disease diagnosis or specific object recognition.

4. How does One-Shot Learning work?

One-Shot Learning typically leverages pre-trained models or meta-learning strategies to capture information from minimal data and generalize it for prediction purposes.

5. What is Siamese Networks in the context of One-Shot Learning?

Siamese Networks are neural networks utilized in One-Shot Learning to measure the similarity between input instances, thereby enabling learning from minimal examples by focusing on similarity/dissimilarity.

6. Can One-Shot Learning be used for classification and regression tasks?

While commonly associated with classification, especially in image recognition, One-Shot Learning can be adapted for regression with appropriate modeling strategies.

7. What challenges are associated with One-Shot Learning?

Challenges include the risk of overfitting due to limited data, difficulty in model evaluation, and ensuring that the model generalizes well to unseen examples.

8. Is One-Shot Learning related to Transfer Learning?

Yes, Transfer Learning, which leverages knowledge from pre-trained models for a related task, can be utilized in One-Shot Learning to aid in learning from limited data.

9. Can One-Shot Learning be applied in Natural Language Processing (NLP)?

While traditionally popular in image-related tasks, One-Shot Learning can also be applied in NLP, especially in scenarios like rare word categorization or limited-sample classification.

10. How to evaluate the performance of a One-Shot Learning model?

 Due to limited data, performance evaluation can be challenging but may involve techniques like Leave-One-Out Cross-Validation (LOOCV) or utilizing synthetic data for validation.

11. Is One-Shot Learning practical for real-world applications?

 Absolutely! One-Shot Learning finds practical applications in domains like facial recognition, medical diagnosis, and any scenario where labeled data is scarce or expensive to obtain.

12. Are there any popular algorithms or frameworks for implementing One-Shot Learning?

 Frameworks like TensorFlow and PyTorch enable One-Shot Learning implementation, and algorithms might involve Siamese Networks, Matching Networks, or utilizing pre-trained models with adaptation layers.

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