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Building AI models with limited Data — discover N-way K-shot learning basics
Machine Learning (ML) is a branch of artificial intelligence (AI) that is dedicated to creating systems that can learn from data and make decisions based on it. It involves a variety of techniques and algorithms that enable computers to recognize patterns and make predictions without needing specific instructions for each task. With ML, computers can analyze data and improve performance over time, making it an increasingly valuable tool in a wide range of applications.
All Machine Learning is based on the foundation of core elements:
- Data — The cornerstone of any ML model. It can be labelled (supervised learning) or unlabeled (unsupervised learning)
- Model — A mathematical representation of a real-world process. Models are trained using algorithms
- Algorithm — A set of rules or instructions given to an ML model to help it learn from data
- Training — The process of teaching a model to make predictions or decisions, usually by feeding it data
Traditionally, all ML models require millions of data points to effectively train and start providing relevant outputs. But what if there are limited data points available? Can Machine Learning be achieved with a limited data set?
The answer
N-way K-shot learning — is a specialized approach in the domain of machine learning, particularly under the umbrella of few-shot learning. This technique is designed to enable a model to learn and make predictions based on a very limited amount of data.
This methodology stands in stark contrast to traditional machine learning, which typically requires large volumes of data to achieve high accuracy.
The “N-way” refers to the number of classes that the model needs to learn, and the “K-shot” refers to the number of examples per class that the model is exposed to during training
Differences from the traditional approach
Few-shot learning is a modern machine learning approach where the model is trained to…
