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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 techniques can also leverage unrelated data sources to improve learning. When multiple source distributions are involved, the problem extends to multi-source domain adaptation. [1] Domain adaptation is a specialized area within transfer learning.
The formal discipline (or mental discipline) approach to learning believed that specific mental faculties could be strengthened by particular courses of training and that these strengthened faculties transferred to other situations, based on faculty psychology which viewed the mind as a collection of separate modules or faculties assigned to various mental tasks.
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]
In 1976 transfer learning was introduced in neural networks learning. [34] [35] Deep learning architectures for convolutional neural networks (CNNs) with convolutional layers and downsampling layers and weight replication began with the Neocognitron introduced by Kunihiko Fukushima in 1979, though not trained by backpropagation. [36] [37] [38]
For example, after completing a safety course, transfer of training occurs when the employee uses learned safety behaviors in their work environment. [1] Theoretically, transfer of training is a specific application of the theory of transfer of learning that describes the positive, zero, or negative performance outcomes of a training program. [2]
All the unique features contribute to a student's ability to use transfer of learning. [22] There are structural techniques that can aid learning transfer in the classroom. These structural strategies include hugging and bridging. [24] Hugging uses the technique of simulating an activity to encourage reflexive learning.
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 ...