Artificial Intelligence
What is Transfer Learning
Transferring knowledge from one task to another
Transfer Learning — an ML technique where a model trained on one task is used as a starting point for solving another task.
Types of Transfer Learning
- Feature extraction — freezing base model, training only top layers
- Fine-tuning — additional training of part or all layers
- Domain adaptation — adapting to a new data domain
- Multi-task learning — training on multiple tasks simultaneously
Advantages
- Less data — no need for huge dataset for new task
- Faster training — not starting from scratch
- Better quality — leveraging knowledge from large dataset
- Resource savings — fewer computations for training
Popular Pre-trained Models
- Images — ResNet, VGG, EfficientNet, CLIP
- Text — BERT, GPT, T5, LLaMA
- Audio — Wav2Vec, Whisper
- Multimodal — CLIP, BLIP, Flamingo
Business Applications
- Image classification — transfer from ImageNet to corporate data
- NLP tasks — transfer from BERT to specific domain
- Healthcare — transfer general model to medical images
- Startups — quick ML launch without large datasets