model transferability
[ˈmɒd.əl ˌtrænz.fərəˈbɪl.ə.ti]
nounfemininepl: transferabilidades de modelo
transferabilidade do modelo
1. The capability or degree to which a machine learning model trained on one dataset, task, or domain can be effectively applied to another different dataset, task, or domain
The model transferability was tested by applying the pre-trained neural network to three different image classification tasks.
A transferabilidade do modelo foi testada ao aplicar a rede neural pré-treinada em três tarefas diferentes de classificação de imagens.
2. The property of a trained model that allows its learned features and knowledge to generalize well when deployed in new contexts or problem domains
High model transferability is crucial for reducing the cost of training separate models for each new application.
A alta transferabilidade do modelo é crucial para reduzir o custo do treinamento de modelos separados para cada nova aplicação.
3. In machine learning, the ability of a model to maintain predictive performance when transferred across different but related tasks or environments
Researchers assessed the model transferability across different languages in natural language processing.
Pesquisadores avaliaram a transferabilidade do modelo em diferentes idiomas no processamento de linguagem natural.
This is a technical term primarily used in artificial intelligence, machine learning, and data science communities in both Brazil and the USA. It became increasingly prominent in the 2010s with the rise of deep learning and transfer learning methodologies. In Brazilian tech hubs like São Paulo, the term is frequently used in startup and research contexts. The concept is crucial in modern AI development as it addresses the practical challenge of maximizing investment in model development across multiple applications.
Related Idioms & Phrases
transfer knowledge across domains
apply a model to new problems
bridge the domain gap
extend model capabilities
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