Zero-Shot Learning (ZSL) is a problem setting in deep learning.At test time, the learner observes samples from classes not observed during training and needs to predict the class they belong to. This problem has been widely studied in the fields of computer vision, natural language processing, and machine perception. The main purpose of Zero-Shot is to gain the ability to predict results without any training samples. The machine must recognize objects from classes that were not trained during training. Zero-shot learning is based on knowledge transfer, which is already included in the examples fed during training.

Importance and Applications of Zero-Shot Learning

• Data labeling is a labor-intensive task, and zero-shot learning can be used when there is a lack of training data for a specific category;
• Zero-shot learning can be deployed in scenarios where the model must learn a new task without relearning previously learned tasks;
• Improve the generalization ability of machine learning models;
• Zero-shot can be a more efficient way to learn new information than traditional methods (e.g. learning by trial and error);
• Zero-shot learning is also helpful for finding visual effects in image classification and object detection;
• Zero Lens also supports the development of multiple deep working frameworks such as image generation and image retrieval.

References

【1】https://en.wikipedia.org/wiki/Zero-shot_learning

【2】https://www.analyticsvidhya.com/blog/2022/12/know-about-zero-shot-one-shot-and-few-shot-learning/#h-zero-shot-learning