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An Introductory Guide to Few-Shot Learning for Beginners

Few-shot learning can also be called One-Shot learning or Low-shot learning is a topic of machine learning subjects where we learn to train the dataset with lower or limited information.

Usually, machine learning models require a lot of data to work fine on their applications. But what is the case where you do not have enough data to make the model learn well? Let’s take an example of any face recognition system where you need to put thousands of photos to make the system learn the faces. That becomes complex and exhaustive work. In such cases, we need to train the model with a lower amount of data. And Few-Shot learning is a concept that can be used for training the models with a lower amount of data. In this article, we will have a detailed overview of the few-shot learning with its approaches and application domains. The major points to be covered in this article are listed below.

Table of Contents

  1. What is Few-Shot Learning?
  2. Why is Few-Shot Learning Important?
  3. Few-shot Learning, Zero-shot Learning, and One-shot learning
  4. Approaches to Few-shot Learning
  5. Applications of Few-shot Learning
  6. Libraries, Packages, and Datasets for Few-Shot Learning

What is Few-Shot learning(FSL)?

Few-shot learning can also be called One-Shot learning or Low-shot learning is a topic of machine learning subjects where we learn to train the dataset with lower or limited information.

Traditional machine learning models need to feed data as much as the model can take and because of large data feeding, we enable the model to predict better. Where this learning aims to build accurate machine learning models with less training data. There can be many benefits of it like it can help in reducing the time costs, computational costs, data analysis costs, etc.

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Why is Few-Shot learning Important?

There are various factors based on them We can tell that this learning is important.

Let us discuss the important points on it.


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  • Training models for rare cases – By using this learning, models can be trained for rare cases like using a model trained on these learning methods for classification of leaves or flower can classify the rare species of leaves and flower by just providing a small amount of the information about the subjects which needs to be identified or classified.
  • Cost-effectiveness-  As we know that storing large amounts of data in databases is highly costly and training models on a large amount of data make the model slow and also the computational cost is also high. Using these learning techniques we can make our machine learning projects less costly with higher performance.
  • In the case of image classification, we require a large amount of data for traditional models to make them work accurately let’s take an example of recognizing the human written and computer-generated text where the human can easily differentiate between them after looking for three four-time and a traditional models will require a very high amount of data for training. Where this learning will aim here to make this recognition with very few amounts of data.just like humans.

Few-shot Learning, Zero-shot Learning, and One-shot Learning

Few-shot learning methods basically work on the approach where we need to feed a light amount of data to model for training. where Zero-shot learning methods work on the approach where zero amount of data for any particular class is used by models to predict correctly. They have a similar application such as:

We can say that the one-shot learning methods are the combination of few-shot learning and zero-shot learning where we use only one instance for training the models. Most of the face recognition system uses the one-shot learning methods for training the model with only one image of the user.

Approaches of Few-shot Learning

We can roughly make few-shot learning models using the following three main approaches 

  • Based on similarity: Models which are based on this approach learn patterns of training data that can be used to separate different categories from the data even when they are not in the data. these learning methods enable us to make ML models to separate two classes that are not present in the training data even in some cases they enable ML models to separate multiple unseen classes. Where the traditional ML models can not discriminate against classes if the information is not provided in training datasets, below are some examples of this type of approach.
  1. Siamese Networks (for discriminating two unseen classes)
  2. Matching Networks (for discriminating multiple unseen classes)
  • Based on learning: Models developed using this kind of approach uses prior knowledge of constraints like hyperparameters and rules which helps in increasing the performance of the model on the data with low information. Below are some examples of this type of approach.
  1. MAML(hyperparameter tuning in low-shot learning)
  2. LSTMs(Learning update rules)
  • Based on data:  Models which are based on this approach exploit prior knowledge of the dataset like the structure of the data and variables of the data. Using this approach we can make a feasible model. Below are some examples of this type of approach
  1. Pen-stroke models(Generative models for families of data classes)
  2. Analogies (Facebook AI Research)

Applications of Few-shot Learning

There can be various fields where the few-shot learning can be used for various applications. application of this learning in different fields are as follows:

  • Computer vision – In the computer vision field, we try to make a machine to learn different inferences of digital data or video data. This learning can be used in solving the following problems of computer vision:

Image processing

  1. Character recognition
  2. Image classification
  3. Object recognition 
  4. Object tracking
  5. Image segmentation

Video processing

  1. Video classification
  2. action prediction
  3. Action localization
  • Natural Language processing – In the natural language processing (NLP) field of machine learning, we try to make a machine understand the human language. This learning can be used in solving the following problems of Natural language processing:
  1. Parsing
  2. Translation
  3. sentiment classification
  4. multi-label text classification 
  • Signal processing – In signal processing, we try to make inferences from the data which consists of signals. Audio files can be considered as signal data. This learning can be used in solving the following problems of signal processing:
  1. Voice cloning
  2. Voice conversion based on language.
  3. Voice conversion based on user
  • Other application – We can use the this learning in other applications also which are out of the machine learning field
  1. In robotics, it can be used for visual navigation for learning the movement of the robots and for continuous control of the robots.
  2. It can be used in medicine for new drug detection.
  3. It can be used in mathematics for curve detection.

Libraries, Packages, and Datasets for Few-Shot Learning

There are various libraries and packages available for performing the FSL. We can use them to make our projects. Some of them are listed below.

  • Libraries 
  1. Pytorch – torchmeta– A collection of extensions and data-loaders for FSL & meta-learning in PyTorch.
  2. Meta-Transfer Learning for Few-Shot Learning-This repository contains the TensorFlow and PyTorch implementations for FSL.
  3. LibFewShot-LibFewShot is a  library for FSL with  various classical FSL approaches
  • Repository
  1. A F-shot learning
  2. FewRel
  3. Prototypical Networks on the Omniglot Dataset
  • Datasets
  1. FewRel Dataset
  2. Omglot dataset

The above-given libraries can be used for making models using the FSL methods, the repository can be used for taking some advantages out of them like learning purposes and modelling purposes. The above-given data sets are benchmark datasets for FSL which can be used for the learning procedure of FSL.

Final Words 

Here in the article, we have discussed how we can make traditional modelling more advanced using few-shot learning. We have also discussed the benefits of this learning including its application. Since it is a great area of research we have various libraries and repositories available to become more acquaintances to the FSL. Also, we listed out some of the repositories related to the FSL.  We also discussed the FSL procedures as the approaches. 

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Yugesh Verma
Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.

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