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name: self-training description: "Operate Transfer Learning Library semi-supervised and self-training workflows using TLLib pseudo-label, consistency, teacher, thresholding, and debiasing APIs." disable-model-invocation: true metadata: disco-role: operating license: MIT

Self-Training Skill

Use this sub-skill when a user needs TLLib guidance for semi-supervised learning or self-training on image classification tasks: pseudo-labeling, Pi Model, Mean Teacher, UDA, FixMatch, FlexMatch, Self-Tuning, DST, DebiasMatch-style thresholding, Noisy Student, or MCC-like self-training regularizers.

Route here for

• Confidence-based pseudo-label selection, consistency losses, weak/strong augmentation training loops, EMA teacher/student updates, dynamic thresholding, class-balance regularization, DST worst-case estimation, and Self-Tuning queues.
• Choosing between Pseudo Label, Pi Model, Mean Teacher, UDA, FixMatch, FlexMatch, Noisy Student, Self-Tuning, DebiasMatch-style debiasing, DST, and MCC-style regularization.
• Safe CPU checks for tllib.self_training losses/helpers without datasets, checkpoints, GPUs, or benchmark training.
• MoCo-style checkpoint conversion planning before SSL fine-tuning.

Route elsewhere

• Datasets, image-list formats, class-balanced labeled subsets, transforms, model factories, metrics, and utility loaders: vision-data-models.
• Task adaptation / fine-tuning regularizers or checkpoint use after conversion: task-generalization.
• Domain-adaptation tasks that use MCC on target-domain predictions: domain-adaptation.

Operating sequence

1. Confirm the task is semi-supervised/self-training: a small labeled split plus a larger unlabeled split, or a teacher/student self-training loop. If the user is adapting across source/target domains, route DA-specific pieces to domain-adaptation.
2. Confirm data/model readiness through vision-data-models. Full SSL training needs datasets, class-balanced labeled sampling, weak/strong augmentations, pretrained backbones or checkpoints, and usually CUDA; this sub-skill only claims CPU component-level verification.
3. Choose the method family:
   • Simple pseudo labels: ConfidenceBasedSelfTrainingLoss.
   • Prediction consistency: L2ConsistencyLoss / ConsistencyLoss.
   • Teacher/student averaging: EMATeacher plus a consistency loss.
   • Weak/strong augmentation: UDA or FixMatch-style losses.
   • Curriculum thresholding: DynamicThresholdingModule for FlexMatch.
   • Self-supervised or contrastive adaptation: Self-Tuning and MoCo checkpoint conversion.
   • Debiasing: DST heads/losses or DebiasMatch-style class-balance/threshold checks.
4. Use the bundled references for API signatures, workflow recipes, checkpoint conversion, and troubleshooting. Do not copy or execute repository benchmark trainers as runtime helpers.
5. For installation/API sanity, run scripts/tllib_self_training_smoke.py from any directory where tllib is installed.

References

• API reference
• Self-training workflows
• Checkpoint conversion
• Troubleshooting

Verification status

The bundled smoke script exercises tiny CPU tensors for pseudo-label selection, consistency, EMA teacher updates, UDA weak/strong loss, MCC, FlexMatch dynamic thresholds, class-balance loss, and DST worst-case estimation. It does not run benchmark training, download datasets, download pretrained checkpoints, perform MoCo conversion on external files, or verify optional timm/CUDA training stacks.