transferlearning and Transfer-Learning-Library
The first is a comprehensive educational repository aggregating papers, datasets, and implementations across multiple transfer learning paradigms, while the second is a focused, production-oriented library providing unified implementations of domain adaptation algorithms—making them complementary resources where researchers might study concepts in the former and apply them using the latter.
About transferlearning
jindongwang/transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Provides curated research papers, implementation code, benchmark datasets, and evaluated models across transfer learning subdomains including unsupervised domain adaptation, domain generalization, and test-time adaptation. Integrates with PyTorch and popular deep learning frameworks, offering implementations for algorithms from recent top-tier conferences (CVPR, NeurIPS, IJCAI) alongside standardized evaluation benchmarks for reproducible comparisons.
About Transfer-Learning-Library
thuml/Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
Built on pure PyTorch with torchvision-consistent API design, the library provides modular implementations of domain alignment (adversarial, MMD-based), domain translation (CycleGAN variants), self-training, and model selection methods organized across seven functional categories. Supports diverse vision tasks including classification, object detection, semantic segmentation, keypoint detection, and person re-identification, with specialized learning setups for domain adaptation, task adaptation, out-of-distribution generalization, and semi-supervised learning scenarios.
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