What Transfer Learning Meaning, Applications & Example
A technique that reuses a model developed for one task for another.
What is Transfer Learning?
Transfer Learning is a machine learning technique where a model trained on one task is reused or adapted for another related task. Instead of training a model from scratch, transfer learning leverages the knowledge learned from a previous task, reducing the amount of data and time required for training a new model.
Types of Transfer Learning
- Inductive Transfer Learning: The model is fine-tuned on a new task using the knowledge from the source task, while the original model architecture remains the same.
- Transductive Transfer Learning: The model uses data from the target task without changing the task-specific labels or output space.
- Unsupervised Transfer Learning: Involves transferring knowledge without labeled data in the target task, typically by learning common representations from data.
Applications of Transfer Learning
- Computer Vision : Transfer learning is commonly used in image classification , where models pre-trained on large datasets like ImageNet can be adapted to specific image classification tasks with less labeled data.
- Natural Language Processing (NLP) : Models like GPT and BERT use transfer learning to apply knowledge from a large corpus to specific language tasks such as text classification or question answering.
- Medical Imaging: Transfer learning can be used to adapt models trained on general image data to specific medical tasks, like detecting tumors in radiology images.
Example of Transfer Learning
In image classification, a model pre-trained on a large dataset such as ImageNet (which contains millions of images from various categories) can be fine-tuned to classify a smaller, specific dataset, such as medical images of skin cancer. The initial layers of the model capture general image features like edges and textures, while the later layers can be fine-tuned to recognize specific patterns related to the new task.