Most deep learning models rely on supervised learning, requiring extensive labeled datasets for training. These models learn by predicting outcomes on labeled data, adjusting their parameters to minimize errors. However, acquiring labeled data is often expensive, time-consuming, or even impossible for certain tasks.
Supervised learning, while effective, faces limitations when dealing with real-world scenarios where labeled data is scarce. Annotating vast amounts of data is costly and impractical. Consider image recognition: humans can distinguish around 30,000 object categories. Training AI models on each category with labeled examples is simply not feasible.
This limitation led to the development of n-shot learning, encompassing few-shot and one-shot learning. These techniques leverage transfer learning and meta-learning to enable models to recognize new classes with minimal labeled examples. Few-shot learning uses a handful of examples, while one-shot learning, as the name suggests, uses only one.
Zero-shot learning (ZSL) defines a learning problem where a model predicts classes it has never seen during training. Unlike few-shot or one-shot learning, ZSL doesn’t rely on any labeled examples of the target classes.
The presence of the unseen class in the training data, albeit unlabeled, isn’t a factor in ZSL. For instance, large language models (LLMs) trained on massive text corpora via self-supervised learning are well-suited for ZSL. These models might have encountered information about unseen classes incidentally, enabling them to make predictions without explicit training. ZSL methods leverage this auxiliary knowledge to generalize to new classes.
Due to its versatility and broad applicability, zero-shot learning is a growing research area in data science, particularly in computer vision and natural language processing (NLP). It offers a promising path towards building AI systems capable of adapting to novel situations without extensive retraining. Zero-shot learning holds potential for applications ranging from image classification and object detection to language translation and text summarization.
