multi-task learning
[/ˈmʌl.ti.tæsk ˈlɜr.nɪŋ/]
noun
aprendizado multitarefa
1. A machine learning approach where a single model is trained to perform multiple related tasks simultaneously, sharing representations and parameters across tasks to improve generalization and efficiency
Multi-task learning allows the neural network to learn shared features that benefit all tasks at once.
O aprendizado multitarefa permite que a rede neural aprenda características compartilhadas que beneficiam todas as tarefas simultaneamente.
2. A training methodology that leverages the relationships between different tasks to improve model performance and reduce overfitting
By using multi-task learning, we can train the model on both object detection and semantic segmentation together.
Ao usar aprendizado multitarefa, podemos treinar o modelo em detecção de objetos e segmentação semântica juntas.
Multi-task learning is a specialized term primarily used in artificial intelligence and machine learning communities in both Brazil and the United States. It reflects the growing importance of AI research in tech hubs like São Paulo and Silicon Valley. The concept represents a paradigm shift in how models are trained to be more efficient and generalizable across related domains.
Related Idioms & Phrases
killing two birds with one stone (metaphorical equivalent for multi-task benefits)
aprender múltiplas tarefas ao mesmo tempo
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