Few-Shot Learning: Everything You Need to Know

Table of contents

• Introduction
• What is few-shot learning?
  
• Why is few-shot learning important?
• What are the factors driving the adoption of few-shot learning?
• What’s the difference between Few-shot learning, Zero-shot learning and One-shot learning?
• What are applications of few-shot learning?
• What does the future look like for few-shot learning ML?

Introduction

So you’re setting up face recognition for yourself on your mobile device and it requires thousands of your pictures to recognize you and unlock your phone. Sounds like a technical nightmare, doesn’t it? But given the need to improve the accuracy of data models, isn’t accruing more and more data only fair?  Of course, the fact that data is the life-blood of any machine learning model and ensures its success holds true. A learning model fed with sufficient and quality data is more likely to yield accurate results. But accruing a lot of data can often get unrealistic and difficult to achieve- given the high costs involved and the ability to manage data.

That’s where a learning model like few-shot learning comes into the picture.

What is few-shot learning?

Few-shot learning or low-shot learning refers to the practice of feeding a learning model with a very small amount of data, contrary to the normal practice of using a large amount of data. The training datasets contain very limited amounts of information. It is mostly implemented in areas  where a model is expected to give appropriate results even without having several training samples, in computer vision, for instance.

Why is few-shot learning important?

A common practice in machine learning is to feed maximum data to the learning model. This is because feeding more data enables better prediction. Few-shot learning, on the other hand,  aims to build accurate machine learning models with training data. It is important because it helps companies reduce cost, time, computation, data management and analysis.

What are the factors driving the adoption of few-shot learning?

Few-shot learning models are driven by the concept that reliable algorithms can be created from minimalist datasets. Here are some driving factors behind its increasing adoption:

• Scarce data: In the event of scarcity of data, supervised or otherwise, machine learning tools often find it challenging to make accurate predictions and make reliable inferences
• Reducing data collection and computational costs: Since few-shot learning model requires less data to train a model, costs related to data collection and labeling can be reduced considerably. Additionally, less training data also means low dimensionality in the training dataset, which adds to reducing the related computational costs
• Rare-case learning: By leveraging few-shot learning, machines can be trained to learn rare cases. For example, when classifying images of animals, an ML model trained with few-shot learning techniques can classify an image of a rare species correctly after being exposed to small amounts of prior information.

What’s the difference between Few-shot learning, Zero-shot learning and One-shot learning?

The aim of implementing few-shot learning is to predict the correct class of instances when a small amount of information is available in the training dataset. Zero-shot learning aims to predict the correct class without being exposed to any instances belonging to that class in the training dataset at all. In zero-shot learning, a learner observes samples from classes that were not observed during training, and predicts the category they belong to. In one-shot learning, the aim is to learn information about object categories from one training sample. The learner is exposed to one instance of each class and is required to make multiple predictions based on it.

What are some applications of few-shot learning?

Given the minimal datasets required and low cost involved, few-shot learning has found uses across multiple areas:

• Computer vision: Few-shot learning is used in computer vision to solve problems related to character recognition, image classification, object recognition, motion prediction, event detection and more
• Natural language processing: FSL enables natural language processing applications to accomplish tasks with limited text data. This includes parsing, translation, sentence completion, etc
• Robotics: To increase robotic intelligence, FSL can be used to train robots. This includes visual navigation, movement imitation, action manipulation and more
• Acoustic Signal Processing: Sounds can be analyzed using ASP augmenting it with FSL can power tasks such as voice cloning, voice modulation, voice conversion to different languages
• Few-shot drug discovery: A research by the Massachusetts Institute of Technology states that FSL can be used to significantly lower the amounts of data required to make effective predictions in drug discovery applications

What does the future look like for few-shot learning ML?

It has become evident that few-shot learning in machine learning is proving to be the best-fit solution whenever training is challenged by the scarcity of data or the costs involved in training data models. A research by IBM predicts that machine learning will evolve around the following 3 major segments in the future:

• Classic ML: One dataset at a time, one task and one heavy training
• Few-shot ML: Heavy offline training, then easy learning on similar tasks
• Developing ML: Continuous learning on various tasks

We can see that machine learning has undergone enormous growth in recent years. The increase in advanced algorithms and learning models, the powerful computing capabilities of machines, and big data management have all contributed significantly to this growth. A point to note here is that we can’t claim yet that ML advancement has reached its pinnacle. We will continue to see more breakthroughs in the form of techniques, optimization and use cases. It is, therefore, in the best interest of businesses to quickly identify their “intelligent” needs and adopt relevant solutions at the earliest.

If you’re a business that’s looking for adoption of AI and ML solutions and needs guidance on various opportunities available for them in this domain, get in touch with us.