What Zero-shot Learning Meaning, Applications & Example
A technique that enables recognition of unseen classes.
What is Zero-shot Learning?
Zero-shot learning (ZSL) is a machine learning technique where a model is able to correctly make predictions on data it has never seen during training. Instead of relying on labeled training examples for every possible category, zero-shot learning enables models to generalize and recognize unseen classes based on their relationship to other known classes.
How Zero-shot Learning Works
- Semantic Embedding: Zero-shot learning often relies on a semantic embedding space, such as word embeddings (e.g., Word2Vec or GloVe) or attributes. This allows the model to understand relationships between known and unknown classes based on shared attributes.
- Class Attributes: During training, the model learns the characteristics of seen classes (such as shape, color, or size), and then uses this information to infer the properties of unseen classes.
- Prediction: When the model encounters a new, unseen class during inference , it compares the semantic features of that class with those of the classes it has already learned and predicts the most likely class label.
Types of Zero-shot Learning
- Attribute-based ZSL: In this approach, each class is described by a set of human-defined attributes (e.g., “has wings” for birds or “is round” for a ball). The model uses these attributes to make predictions for unseen classes.
- Text-based ZSL: This method uses textual descriptions (e.g., natural language) to describe classes. The model can match the textual description of an unseen class with those of the seen classes.
Applications of Zero-shot Learning
- Image Classification: Zero-shot learning can be used to classify images into categories the model has never seen during training by leveraging shared attributes or descriptions.
- Natural Language Processing: In NLP, zero-shot learning enables models like GPT and BERT to perform tasks like sentiment analysis or question answering on unseen classes without requiring task-specific data.
- Recommendation Systems: Zero-shot learning can help recommend items to users based on general knowledge or user preferences, even if the specific item was not seen in training data.
Example of Zero-shot Learning
In image recognition , a model might be trained to recognize categories like “cat,” “dog,” and “car.” Using zero-shot learning, the model can correctly classify a new object, such as a “zebra,” by understanding that it shares attributes (e.g., stripes, four legs) with similar classes and predicting it as a new category without having seen a zebra during training.