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name: transfer-learning-library description: "Use Transfer Learning Library (TLLib) for PyTorch transfer learning, domain adaptation, domain generalization, task adaptation, self-training, model selection, and vision data/model workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Transfer Learning Library (TLLib)

Use this repo skill when a task names Transfer Learning Library, TLLib, tllib, or asks for PyTorch transfer-learning workflows such as domain adaptation, domain generalization, task adaptation/fine-tuning, self-training, model ranking, or TLLib vision datasets/models.

First checks

1. Confirm the package imports before following any workflow:

   python - <<'PY'
   import tllib, torch, torchvision, numpy
   print('tllib', tllib.__version__)
   print('torch', torch.__version__)
   print('torchvision', torchvision.__version__)
   print('numpy', numpy.__version__)
   PY
   

2. For TLLib 0.4, prefer an older compatible stack when model factories or losses fail under modern dependencies: Python 3.8, PyTorch/TorchVision from the 1.8/0.9 era, and numpy<1.24 are known-good inspection choices.

3. Run the bundled root smoke check when diagnosing install/import problems:

   python scripts/check_tllib_install.py
   

4. Read references/repo-provenance.md before deciding whether this skill is current for a checkout. Read references/troubleshooting.md for install/import, dependency, dataset, and backend failures.

Route by task

User task	Read
Unsupervised/partial/open-set/domain-adversarial adaptation; DANN, CDAN, DAN, JAN, MDD, MCD, MCC, AFN, BSP, RegDA, D-adapt, WILDS adaptation	sub-skills/domain-adaptation/SKILL.md
Dataset roots, ImageList text files, domain names, transforms, vision model factories, re-id/keypoint/segmentation utilities, metrics/loggers	sub-skills/vision-data-models/SKILL.md
Domain generalization or fine-tuning/task adaptation; MixStyle, IBN, StochNorm, GroupDRO, IRM/VREx/MLDG, L2-SP, DELTA, BSS, Co-Tuning, LwF, Bi-Tuning	sub-skills/task-generalization/SKILL.md
Semi-supervised learning or self-training; pseudo-labeling, Pi Model, Mean Teacher, UDA, FixMatch/FlexMatch, MCC, Self-Tuning, DST, Noisy Student	sub-skills/self-training/SKILL.md
Rank or choose pretrained models by transferability metrics; H-score, regularized H-score, LEEP, NCE, LogME, TransRate	sub-skills/model-selection/SKILL.md
CycleGAN/FDA/CyCADA/SPGAN image or domain translation components	sub-skills/translation/SKILL.md

Use references/capability-map.md when the user names an algorithm and you need to find its TLLib module, owning sub-skill, optional dependencies, or validation route.

Operating boundaries

• This skill teaches the installed tllib package and distilled workflow patterns. It does not require the original repository checkout at runtime.
• Original benchmark examples are treated as evidence only. Their reusable behavior is distilled into references and bundled scripts; do not ask future users to open or run source-tree examples.
• CPU smoke checks validate public APIs and tensor/array contracts. Real benchmark training is usually long-running, dataset-dependent, and GPU-recommended.
• Object detection adaptation, WILDS, text/molecule examples, and some DG trainers need optional stacks such as Detectron2, MMCV, WILDS, TensorFlow, Transformers, Torch Geometric, OGB, timm, or higher. Install only the optional stack that the user explicitly needs.
• Dataset downloads are external and may be broken or license-restricted. Prefer user-provided local datasets and validate layouts with the vision-data-models scripts.

Bundled root assets

• scripts/check_tllib_install.py verifies imports, version compatibility, representative public APIs, and optional backend visibility without downloading data.
• references/troubleshooting.md covers cross-cutting failures before routing to workflow-specific troubleshooting.
• references/repo-routing-metadata.json contains structured metadata for managed repo-skill routing.