Transfer learning is a popular technique in the field of machine learning, allowing models to leverage knowledge from one task to improve performance on another related task. Below is a curated list of papers on transfer learning, categorized for easy reference.
Categorization
Foundational Papers
- Understanding Deep Learning - A comprehensive overview of deep learning concepts, essential for understanding transfer learning.
- ImageNet Classification with Deep Convolutional Neural Networks - This seminal paper introduces the concept of using pre-trained networks for transfer learning.
Applications in Computer Vision
- Very Deep Convolutional Networks for Large-Scale Image Recognition - Discusses the effectiveness of deep convolutional networks in image recognition tasks.
- Transfer Learning for Object Detection - A paper focusing on applying transfer learning to object detection problems.
Applications in Natural Language Processing
- A Theoretically Grounded Application of Pretrained Representations for Text Classification - Explores the use of pretrained representations for text classification tasks.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - Introduces BERT, a transformer-based model for natural language understanding.
Conclusion
Transfer learning has revolutionized the field of machine learning by enabling models to learn from large datasets and transfer that knowledge to smaller, more specialized tasks. The papers listed above provide a solid foundation for understanding and implementing transfer learning in various domains.
Transfer Learning Diagram