Zero Shot Learning

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Zero Shot Learning

What is Zero Shot Learning?

Zero-shot learning serves as the cornerstone for the development of generative AI methods.

Zero-shot learning, along with one-shot learning, constitutes a methodology enabling generative AI models to grasp and categorize novel instances despite having access to only a restricted volume of training data. This concept pertains to the model’s capability to classify novel, unseen instances belonging to categories that were absent from the training dataset.

Examples of zero-shot learning:

Image Recognition and captioning: For example, a model that has been trained on images of dogs and cats could potentially identify an image of a tiger by drawing parallels with analogous attributes present in its training dataset.
Natural Language Processing (NLP): Zero-shot learning facilitates comprehension and generation of previously untrained text. Its utility is pronounced in tasks like translation, sentiment analysis, and text generation.
Medical Diagnosis: Zero-shot learning allows the model to recognize illnesses or ailments that it lacks training data for.
Object detection: Zero-shot learning enables the identification of objects in images or videos that the model isn’t trained to identify. For instance, a model focused on cars and trucks could potentially spot a bicycle within an image, despite lacking bicycle-specific training.
Computational Biology: It helps forecast traits of unobserved biological entities. For instance, it can predict the characteristics of a novel virus strain using its genetic sequence and supplemental data about analogous viruses.

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