Naveen Kumar Thawait

Transfer learning improves model performance across different tasks and domains using limited labeled data. You can explore different techniques include domain adaptation, zero-shot learning, few-shot learning, and cross-modal transfer.

In domain adaptation, pre-trained models are fine-tuned on domain-specific data, while few-shot learning trains models with minimal examples per class. Cross-modal transfer extends knowledge between different data modalities. These approaches facilitate better generalization by leveraging pre-existing knowledge from related tasks and domains.

It's a bit complicated to explain as a short reply, but to improve transfer learning for better generalization (especially when you have limited labeled data), I would start with pre-trained models on large datasets. Then simply fine tune the model on the new task with a small learning rate. Also feel free to enhance the dataset with augmentation and use regularization techniques wie dropout or weight decay to prevent overfitting.

To increase transfer learning for greater generalization across tasks and domains with less labeled data, emphasis on domain adaptation through adversarial training, few-shot learning with meta-learning, and self-supervised learning on huge unlabeled datasets. Enhance data with modern augmentation techniques and employ regularization approaches to prevent overfitting. Leverage knowledge distillation from larger models and add multi-task learning to exchange representations across activities. Combining these approaches can considerably boost transfer learning efficacy in varied applications. Naveen Kumar Thawait