methodology — Page 2

Transfer Learning

What is Transfer Learning? Transfer learning is a machine learning technique where an already trained model is utilized to solve a different but related problem. The concept of transfer learning is to leverage the knowledge gained from a previously trained algorithm to help another algorithm solve a related problem efficiently, quickly, and accurately. Transfer learning is a valuable tool for machine learning because it allows developers, researchers, and designers to train accurate models for

Zero-Shot Learning

Zero-shot learning, or ZSL, is a model's ability to detect classes that it has never seen before during training. This means that even if the classes are not known during supervised learning, the model can still identify them through other means. How ZSL Works Earlier approaches in ZSL use attributes in a two-step approach to infer unknown classes. In computer vision, more recent advances learn mappings from the image feature space to semantic space. This involves learning how to identify ima

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