Generative Adversarial zero-shot

learning is a type of machine learning technique that involves training two deep neural networks: a generator network and a discriminator network. The generator network generates new data samples, such as images or text, while the discriminator network learns to differentiate between real and generated data samples.

Zero-shot learning refers to the task of learning to recognize objects or concepts that are not seen during training. In other words, the model is able to recognize new objects based on their relationship to previously seen objects, without any additional training.

Generative Adversarial zero-shot learning combines these two techniques to generate new data samples for unseen objects or concepts. The generator network is trained to generate samples for unseen objects, while the discriminator network is trained to recognize these new samples as belonging to the correct unseen object class. This allows the model to recognize new objects without requiring additional training data, making it a useful technique for applications such as image classification and natural language processing.