GDSC IIT Indore

Zero-Shot Learning Zero-shot learning is a problem setup in machine learning where, at test time, a learner observes samples from classes that were not observed during training and needs to predict the class they belong to. The general idea of zero-shot learning is to transfer the knowledge in the training instances to test instance classification. Thus, zero-shot learning is a subfield of transfer learning. Zero-shot learning has applications in image classification, natural language processing, and more. Zero-shot learning has many potential applications in domains where labeled data is scarce or expensive, such as medical imaging, natural language understanding, speech recognition, etc. This is useful for scenarios where obtaining labeled data for every possible class is impractical or impossible, such as classifying all animal species or natural languages. One of the challenges of zero-shot learning is representing unseen classes so that the model can understand and relate to the s