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Illustration of transfer learning. Transfer learning ( TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
Transfer of learning. Transfer of learning occurs when people apply information, strategies, and skills they have learned to a new situation or context. Transfer is not a discrete activity, but is rather an integral part of the learning process. Researchers attempt to identify when and how transfer occurs and to offer strategies to improve ...
Situated learning is a theory that explains an individual's acquisition of professional skills and includes research on apprenticeship into how legitimate peripheral participation leads to membership in a community of practice. Situated learning "takes as its focus the relationship between learning and the social situation in which it occurs".
Language transfer is the application of linguistic features from one language to another by a bilingual or multilingual speaker. Language transfer may occur across both languages in the acquisition of a simultaneous bilingual, from a mature speaker's first language (L1) to a second language (L2) they are acquiring, or from an L2 back to the L1. [1]
A common test for negative transfer is the AB-AC list learning paradigm from the verbal learning research of the 1950s and 1960s. In this paradigm, two lists of paired associates are learned in succession, and if the second set of associations (List 2) constitutes a modification of the first set of associations (List 1), negative transfer ...
Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals, rather than relying on external labels provided by humans. In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships within the input ...
Synthetic data is increasingly being used for machine learning applications: a model is trained on a synthetically generated dataset with the intention of transfer learning to real data. Efforts have been made to enable more data science experiments via the construction of general-purpose synthetic data generators, such as the Synthetic Data ...
Articulation (education) Articulation, or more specifically course articulation, is the process of comparing the content of courses that are transferred between postsecondary institutions [1] such as TAFE institutes, colleges or universities. In other words, course articulation is the process by which one institution matches its courses or ...
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