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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.
Domain adaptation is a specialized area within transfer learning. In domain adaptation, the source and target domains share the same feature space but differ in their data distributions. In contrast, transfer learning encompasses broader scenarios, including cases where the target domain’s feature space differs from that of the source domain(s).
Knowledge transfer icon from The Noun Project. Knowledge transfer refers to transferring an awareness of facts or practical skills from one entity to another. [1] The particular profile of transfer processes activated for a given situation depends on (a) the type of knowledge to be transferred and how it is represented (the source and recipient relationship with this knowledge) and (b) the ...
Zero transfer occurs when prior learning has no influence on new learning. Near: Near transfer occurs when many elements overlap between the conditions in which the learner obtained the knowledge or skill and the new situation. Far: Far transfer occurs when the new situation is very different from that in which learning occurred. Literal ...
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
Generalization is understood to be directly tied to the transfer of knowledge across multiple situations. [3] The knowledge to be transferred is often referred to as abstractions, because the learner abstracts a rule or pattern of characteristics from previous experiences with similar stimuli. [2]
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 results and thus the learning rate of the second list ...
Multi-task Bayesian optimization is a modern model-based approach that leverages the concept of knowledge transfer to speed up the automatic hyperparameter optimization process of machine learning algorithms. [8] The method builds a multi-task Gaussian process model on the data originating from different searches progressing in tandem. [9]