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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.
Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was inputted directly, compare transfer learning. [1] In machine learning, feature learning or representation learning [2] is a set of techniques that allows a system to automatically discover the representations needed for ...
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained 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" (or, not changed during the backpropagation ...
Zero-shot learning ( ZSL) is a problem setup in deep learning where, at test time, a learner observes samples from classes which were not observed during training, and needs to predict the class that they belong to. The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only ...
T5 (language model) T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI. Introduced in 2019, [ 1] T5 models are trained on a massive dataset of text and code using a text-to-text framework. The T5 models are capable of performing the text-based tasks that they were pretrained for.
Distinction between usual machine learning setting and transfer learning, and positioning of domain adaptation. Domain adaptation [1] [2] [3] is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning a model from a source data distribution and applying that model on a different (but related ...
e. Machine learning ( ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. [ 1] Recently, artificial neural networks have been able to surpass many previous approaches in ...
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.