Construct · 1 fields · 1 citations

feature transferability: a standalone construct

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Machine learning

how well features learned in each layer of a network carry over to a new task, falling from general early layers to task-specific later layers.

In machine learning, feature transferability refers to how well features learned in each layer of a network carry over to a new task, falling from general early layers to task-specific later layers. This meaning comes from Yosinski (2014) in arXiv.[1]

Unit
Recovery means
Measured by

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Nearby constructs

carry1 fields · 1 citationsanchor · a standalone construct feature2 fields · 4 citationsjingle · one word, two constructs layer3 fields · 5 citationsjingle · one word, three constructs

References

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? arXiv. https://doi.org/10.48550/arxiv.1411.1792

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