Term
Transfer Learning (ˈtræns-fər ˈlɜːn-ɪŋ)
Definition
Transfer learning is a method in artificial intelligence where you take a model that has already been trained for one task and tweak it to perform a new, related task.
Where you’ll find it
In AI platforms, transfer learning is often found in model training settings or toolkits. It is widely supported across various AI frameworks like TensorFlow or PyTorch, making it accessible whether you're working on simple applications or complex projects.
Common use cases
- Quickly enhancing performance: Improve an AI model’s efficiency or accuracy when data is limited.
- Reducing resources: Save time and computational resources by using a model that already understands related tasks.
- Experimenting with new applications: Try different data sets or tasks without starting from scratch.
Things to watch out for
- Data relevance: Ensure the original model’s training data is similar to your new dataset to make effective use of transfer learning.
- Overfitting: The model may overfit to the new task if not properly adjusted.
- Integration issues: Specifics of model architectures can sometimes make transfer learning trickier than expected.
Related terms
- Pre-trained Model
- Fine-tuning
- Model Optimization
- Hyperparameter Tuning
- TensorFlow, PyTorch
Pixelhaze Tip: To get the most out of transfer learning, start with a model pre-trained on a task as closely related as possible to your target. This approach significantly enhances the learning process and output quality with minimal extra effort.