#transfer-learning

Master transfer learning and domain adaptation techniques for leveraging pre-trained models across different domains and tasks. Transfer learning strategies: 1. Feature extraction: freeze pre-trained layers, train classifier only, computational efficiency. 2. Fine-tuning: unfreeze layers gradually, lower learning rate (1e-5), task-specific adaptation. 3. Progressive unfreezing: layer-by-layer unfreezing, gradual adaptation, stability preservation. Pre-trained model selection: 1. Computer vision: ImageNet pre-training, ResNet/EfficientNet models, architecture matching. 2. Natural language: BERT/RoBERTa/GPT models, domain-specific pre-training, multilingual models. 3. Audio processing: wav2vec, speech pre-training, audio classification transfer. Domain adaptation methods: 1. Supervised adaptation: labeled target data, direct fine-tuning, small dataset scenarios. 2. Unsupervised adaptation: domain adversarial training, feature alignment, no target labels. 3. Semi-supervised: few labeled target samples, self-training, pseudo-labeling techniques. Advanced techniques: 1. Multi-task learning: shared representations, task-specific heads, joint optimization. 2. Meta-learning: few-shot adaptation, MAML (Model-Agnostic Meta-Learning), rapid adaptation. 3. Continual learning: catastrophic forgetting prevention, elastic weight consolidation. Domain shift handling: 1. Distribution mismatch: covariate shift, label shift, concept drift detection. 2. Feature alignment: maximum mean discrepancy (MMD), CORAL, deep domain confusion. 3. Adversarial adaptation: domain classifier, gradient reversal, minimax optimization. Evaluation strategies: target domain performance, source domain retention, adaptation speed, few-shot learning capabilities, cross-domain generalization assessment for robust transfer learning systems.