Zero-shot learning (ZSL) and traditional transfer learning are two approaches used in machine learning to improve model performance on tasks with limited or no training data. The main difference between them lies in how they handle the training and testing phases. In traditional transfer learning, a model that has been pre-trained on a large dataset is fine-tuned on a related but smaller dataset. This allows the model to transfer knowledge from one task to a different but similar task. For example, a model trained on recognizing objects in everyday photos can be fine-tuned to identify specific types of animals with only a small number of examples.
In contrast, zero-shot learning approaches the problem differently by aiming to recognize classes that were not seen during training. Instead of requiring samples from the target class, ZSL uses additional information about the class to make inferences. For instance, if a model has been trained on several types of animals, but has never seen a zebra, it can still identify a zebra by understanding attributes associated with it, such as "striped," "four-legged," and "horse-like." Zero-shot learning leverages semantic relationships or descriptions to make predictions in scenarios where training data is completely absent.
Both techniques can be beneficial depending on the situation. If you have a small amount of data for the new task, traditional transfer learning is often more effective because the model can leverage its past knowledge. However, in cases where it’s impossible to gather any data for specific classes, zero-shot learning provides a way to extend the model's capabilities without any additional training data. Thus, the choice between these two methods typically depends on the availability of labeled data and the specific requirements of the project at hand.