Translations:Transfer Learning/15/en: Difference between revisions

Latest revision as of 23:34, 27 April 2026

Information about message (contribute)

This message has no documentation. If you know where or how this message is used, you can help other translators by adding documentation to this message.

Message definition (Transfer Learning)

ImageNet-pretrained convolutional networks (ResNet, EfficientNet, ViT) serve as standard backbones. Lower layers learn universal features such as edges and textures, while higher layers learn task-specific patterns. {{Term|fine-tuning}} an ImageNet model on a medical imaging dataset with only a few thousand images routinely outperforms training from scratch.

ImageNet-pretrained convolutional networks (ResNet, EfficientNet, ViT) serve as standard backbones. Lower layers learn universal features such as edges and textures, while higher layers learn task-specific patterns. fine-tuning an ImageNet model on a medical imaging dataset with only a few thousand images routinely outperforms training from scratch.

Revision as of 22:05, 27 April 2026 (view source) FuzzyBot (talk \| contribs) (Importing a new version from external source) Tag: Manual revert ← Older edit		Latest revision as of 23:34, 27 April 2026 (view source) FuzzyBot (talk \| contribs) (Importing a new version from external source) Tag: Manual revert
Line 1:		Line 1:
	ImageNet-pretrained convolutional networks (ResNet, EfficientNet, ViT) serve as standard backbones. Lower layers learn universal features such as edges and textures, while higher layers learn task-specific patterns. ~~Fine~~-tuning an ImageNet model on a medical imaging dataset with only a few thousand images routinely outperforms training from scratch.		ImageNet-pretrained convolutional networks (ResNet, EfficientNet, ViT) serve as standard backbones. Lower layers learn universal features such as edges and textures, while higher layers learn task-specific patterns. {{Term\|fine-tuning}} an ImageNet model on a medical imaging dataset with only a few thousand images routinely outperforms training from scratch.