Zero-Shot Learning (ZSL) is a branch of Machine Learning that enables pre-trained deep learning models to generalize and accurately classify novel classes of data distinct from the training set. For example, a model trained on images of cats and dogs can be applied to identify images of birds. The training samples belonging to the “seen” classes indicate the knowledge already present in the dataset, while the “unseen” instances denote the new class of objects to be identified by the deep learning model. Zero-shot learning, therefore, constitutes an important field of transfer learning research.
Applications of Zero-shot Learning
- Zero-shot learning can identify previously unknown objects in image classification tasks. This can benefit autonomous vehicles, allowing them to react to new entities on the road.
- In natural language processing (NLP), zero-shot learning can classify text data and predict novel classes or entities not seen before. This can be advantageous in chatbot systems and virtual assistants where users may ask questions related to unknown topics.
- Recommender systems that suggest products or services based on a user’s past activities can make suggestions about items unexplored by the user.
- Fraud detection systems can detect patterns of fraudulent behavior that had not been previously encountered. This is a useful approach for real-time fraud detection systems which require prompt reactions.
- Speech recognition systems can recognize unfamiliar languages or accents.
