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name: task-generalization description: "Operate Transfer Learning Library domain-generalization and task-adaptation workflows using TLLib regularization, normalization, and reweighting APIs." disable-model-invocation: true metadata: disco-role: operating license: MIT

Task Generalization Skill

Use this sub-skill when a user needs TLLib guidance for domain generalization or task adaptation / fine-tuning rather than unsupervised domain adaptation.

Route here for

• Domain generalization with source-domain-only training: ERM baselines, MixStyle, IBN, StochNorm-style normalization, CORAL as a DG penalty, GroupDRO, IRM, VREx, and MLDG-style meta-learning patterns.
• Task adaptation / fine-tuning regularizers: L2, L2-SP, DELTA, BSS, Co-Tuning, LwF, StochNorm, and Bi-Tuning.
• Safe CPU-level checks of tllib.regularization, tllib.normalization, tllib.reweight.groupdro, and CORAL loss components.
• Checkpoint conversion planning for MoCo-style pretraining outputs before fine-tuning.

Route elsewhere

• Datasets, image-list formats, dataloaders, model factories, transforms, and metrics: vision-data-models.
• Unsupervised, partial, open-set, or adversarial domain adaptation: domain-adaptation.
• Semi-supervised learning, pseudo-labeling, Mean Teacher, FixMatch/FlexMatch, UDA, DST, and Noisy Student workflows: self-training.

Operating sequence

1. Identify whether the task is DG (train on labeled source domains and test on held-out unseen domains) or task adaptation (fine-tune a pretrained model on a new labeled target task).
2. Confirm data/model readiness through vision-data-models before discussing full training. The benchmark-scale workflows require datasets, pretrained weights, and usually CUDA; this sub-skill only claims CPU component-level verification.
3. Choose the method family:
   • DG style/normalization: MixStyle or IBN.
   • DG robust objective: GroupDRO, IRM, VREx, MLDG, or CORAL penalty.
   • Fine-tuning regularization: L2-SP/DELTA/BSS/Co-Tuning/LwF/StochNorm/Bi-Tuning.
4. Use the bundled references for implementation details and warning checks; do not copy or execute repository benchmark trainers as runtime helpers.
5. For installation/API sanity, run scripts/tllib_task_generalization_smoke.py from any directory where tllib is installed.

References

• API reference
• Domain-generalization workflows
• Task-adaptation workflows
• Checkpoint conversion
• Troubleshooting

Verification status

The bundled smoke script exercises small CPU tensors for regularization, normalization, reweighting, and CORAL components. It does not run benchmark training, download datasets, download pretrained checkpoints, or verify optional timm, higher, WILDS, Detectron2, or CUDA training stacks.