Types of Transfer Learning
Types of Transfer Learning
Transfer learning can be categorized based on the relationship between source and target domains and tasks:
1. Inductive Transfer Learning:
- The target task is different from the source task;
- Example: using an image classifier trained on animals to classify medical images.
2. Transductive Transfer Learning:
- The source and target tasks are the same, but the domains differ;
- Example: sentiment analysis on English reviews (source) and French reviews (target).
3. Unsupervised Transfer Learning:
- Both tasks are unsupervised (e.g., clustering), but domains differ.
Notation:
DSTS=DT=TT(domains differ)(tasks differ)Feature reuse: often, early layers of a neural network learn general features (e.g., edges in images) that can be reused across tasks.
The choice of transfer learning type depends on how similar the source and target domains and tasks are.
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Can you explain the differences between these types in more detail?
What are some real-world applications of each type of transfer learning?
How does feature reuse work in practice?
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Types of Transfer Learning
Transfer learning can be categorized based on the relationship between source and target domains and tasks:
1. Inductive Transfer Learning:
- The target task is different from the source task;
- Example: using an image classifier trained on animals to classify medical images.
2. Transductive Transfer Learning:
- The source and target tasks are the same, but the domains differ;
- Example: sentiment analysis on English reviews (source) and French reviews (target).
3. Unsupervised Transfer Learning:
- Both tasks are unsupervised (e.g., clustering), but domains differ.
Notation:
DSTS=DT=TT(domains differ)(tasks differ)Feature reuse: often, early layers of a neural network learn general features (e.g., edges in images) that can be reused across tasks.
The choice of transfer learning type depends on how similar the source and target domains and tasks are.
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