Zero, Few Shot and One Shot Learning
Zero-shot, few-shot, and one-shot learning techniques allow ML models to predict based on limited labeled data.
Zero-shot learning is a technique where a model can make predictions for classes or categories it has never seen during training. Unlike traditional supervised learning, where all classes are explicitly provided in the training data, zero-shot learning enables a model to generalize to unseen classes based on its understanding of relationships between known classes.
This approach might need human intervention. Models learn through knowledge transfer, which is already accessible in the instances fed while training. This method is proposed to learn intermediate semantic relationships, properties, and contextual information, then apply it to predict a new class of unseen data.
Zero-shot learning practices are useful in computer vision and machine perception.
We can recognize a horse with the help of provided attributes and apply the same knowledge to identify unicorn images with additional features - horned animal and color.
One Shot Learning
One-shot learning is a machine learning approach in which an algorithm is trained to recognize or classify objects with only one example per category. Unlike traditional methods that require a substantial amount of data for training, one-shot learning aims to generalize from a minimal dataset.
Facial recognition systems are typical examples of one-shot learning where models learn face embedding - a rich low-dimensional feature representation.
What is a few-shot learning technique?
Few shot learning(FSL): This is sometimes called low-shot learning (LSL).In this technique, models are fed with very minimal data. While the common practice is using loads of data for model learning, few-shot methods aim to drive accurate predictions through models trained on lesser data.
OpenAI mainly implements this technique as GPT3 is a few-shot learner.
Meta-Transfer Learning for Few-Shot Learning paper proposes a framework for addressing challenges in few-shot learning settings. After that, few-shot learning is also known as a meta-learning problem.
Why is Few-shot Learning Important?
Few shot-learning approach helps with scenarios such as
Learning for Data-scarce Samples
In many real-world scenarios, collecting and labeling a vast amount of data for training can be challenging, time-consuming, and expensive. E.g., rare plant species or diseases. A model designed with fewer data points can help with classification tasks where sufficient data points are unavailable.
Few-shot learning is closely related to transfer learning, where knowledge gained from one task can be applied to another. This transferability is particularly valuable in cases with some knowledge overlap between tasks.
Humans can often learn new concepts or tasks from just a few examples. Few-shot learning attempts to mimic this rapid learning ability, bringing AI models closer to human-like intelligence in certain aspects.
Few-shot learning helps AI research by exploring methods that rely on minimal data. This can lead to the development of novel algorithms and techniques that have broader implications for the field.
Reduce Computational Costs
As models are trained on minimal data, this requires minimal data collection and labeling effort, reducing overall computational costs.
Applications of Few Shot Learning
Three techniques discussed on this page, few shot, one shot, and zero shot learning, come with advantages and some limitations. However, we can employ these methods in limited data availability scenarios.