Methods › Computer Vision › Convolutional Neural Networks › Partial Hybrid Transfer Learning
Partial Hybrid Transfer Learning
Introduced by Tushar Talukder Showrav et al. in Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
While the typical approach to leverage transfer learning in image segmentation models involves replacing the entire encoder, this can restrict the customization and unique strengths of the main network. To address this limitation, we propose a hybrid transfer learning strategy that incorporates pre-trained convolutional layers from two distinct architectures (e.g., VGG16 and DenseNet201), allowing us to leverage the advantages of transfer learning while preserving the core features and characteristics of the base model's architecture.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN 20 May 2024 · 1 repository
Tasks archive 2025-07-28
6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Generative Adversarial Network | 1 |
| Image Segmentation | 1 |
| Medical Image Segmentation | 1 |
| Segmentation | 1 |
| Semantic Segmentation | 1 |
| Transfer Learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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