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What are the five types of transfer learning?
What is the transfer of learning with an example?
Which is an example of transfer learning?
What are the 4 types of machine learning?
Training ML models from scratch can be time-consuming and data-heavy. Transfer learning lets you reuse pre-trained models, saving time and improving outcomes. Learn about key types, real-world use, and how to apply it effectively in your projects.
Building machine learning models from scratch is often slow, resource-intensive, and frustrating—especially when you don’t have enough labeled data or compute power. Training a model for every new task can feel like reinventing the wheel.
Transfer learning solves this by reusing a trained model from a related task. It enables you to apply knowledge gained from one domain to another, reducing training time and enhancing results—even with limited data. By leveraging pre-trained models, developers can focus more on solving the target task and less on gathering massive datasets.
You begin with a pre-trained model (source) that has been trained on a large dataset (often in computer vision or natural language processing). You then:
Use it as a fixed feature extractor (keeping frozen layers) to extract task-specific features.
Or apply fine-tuning, retraining some layers so the trained model adapts to the target domain/target task data.
Early neural network layers learn low-level features, such as edges or textures; later layers encode task-specific features. Transfer learning reuses early layers while modifying later ones, allowing a model trained on one task to serve a new task with improved performance.
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Have you ever wondered how we can take a model trained on one task and apply it to another with remarkable efficiency? 🤔 Enter the fascinating world of transfer learning, where linear algebra plays a pivotal role! — LinkedIn Post
- By Prasad Sawant
Let’s explore the five well‑recognized types of transfer learning:
Domain adaptation addresses situations where the source and target domains share the same feature space but differ in data distribution (i.e., P(Xₛ) ≠ P(Xₜ)). A model trained on one set of inputs can be adapted to another through rebalancing or domain-invariant representation learning.
Applications include sentiment analysis across languages: using reviews from one domain to classify another where labeled data exists only in the source domain (transductive transfer learning).
For example, training a sentiment analysis (target task) model using labeled English reviews (source domain), then applying it to classify unlabeled French reviews with domain adaptation techniques.
Domain confusion aims to align representations so that features become domain invariant. This helps when transferring task-specific features learned by a trained model across domains, nudging the model to treat source and target similarly during the learning process.
In multitask learning, a single neural network learns multiple tasks concurrently rather than transferring from one to another sequentially. Though sometimes debated as technically transfer learning, it still involves knowledge gained from one task helping other tasks via shared hierarchical representations.
A form of inductive transfer learning, one-shot learning enables a model to generalize from one or a very small number of labeled examples. This is particularly useful for object detection or image classification, where data scarcity prevents the collection of more labeled data. The model relies on knowledge gained from other classes to identify new examples.
Here, the model handles new tasks or classes without any labeled examples. It exploits extra information, such as textual descriptions or class embeddings. It transfers representations learned from source tasks to recognize unseen categories (unsupervised transfer learning scenario).
Another way to classify transfer learning:
Inductive Transfer Learning: The source and target tasks differ, and some labeled data exist in the target domain. Includes fine-tuning or multitasking setups.
Transductive Transfer Learning (covered above): Same task in different domains; labeled only in the source.
Unsupervised Transfer Learning: Both domains lack labels; focus on feature extraction or clustering without supervision.
Transfer learning is widely used in computer vision tasks, including:
Image Classification
Object Detection
Facial Recognition
Example Use Case:
A model is initially trained on ImageNet (source data).
It is then fine-tuned for medical imaging classification (target task).
This approach applies both domain adaptation and inductive transfer learning.
It significantly reduces the need for large training datasets in healthcare applications.
Popular pre-trained models used:
◦ ResNet
◦ Inception
In NLP, pre-trained models serve as the backbone for many tasks:
Foundation Models Used:
◦ BERT
◦ GPT
◦ Word2vec
◦ GloVe
Applications:
Sentiment Analysis
Question Answering
Text Summarization
Benefits:
Enables better performance on target tasks with limited labeled data.
Avoids training from scratch, saving computational resources and time.
Use Case:
◦ Transfer learning is used to classify medical images with high accuracy.
◦ Models trained on large image datasets are fine-tuned for specialized tasks (e.g., tumor detection).
Benefit:
◦ Reduces reliance on large, labeled medical datasets, which are often difficult to obtain.
Transfer learning enables agents to transfer knowledge between environments that share some similarities.
Speeds up the learning process in complex or dynamic tasks.
Learn more about Reinforcement Learning
Sentiment analysis models trained in one language are transferred to another language.
Reduces the need to collect labeled data for every individual language.
Supports multilingual AI applications using shared language representations.
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Reduces reliance on substantial data by leveraging pre-trained models and general representations.
Cuts down computational resources and training time.
Enhances model performance even with limited data through fine-tuning or feature reuse.
Using trained model weights from previous tasks allows adaptation without full initial training. The computational resources required are far lower when calibrating a pre-trained network compared to training a model from scratch.
Transfer learning is most suitable when:
You have limited labeled data in the target domain.
Existing models trained on related tasks or domains are available.
The input data or task is similar enough for knowledge transfer.
Avoid using this approach if the source and target tasks are unrelated, as negative transfer may degrade performance.
Keep early layers frozen and replace the final classification layer. The trained model acts as a feature extractor; you train only the new head on target data.
Freeze most layers, but fine-tune the later layers or the entire top block later. This adapts weights from the pre-trained model to learn task-specific features for the new task.
A pretrained ResNet model serves as the backbone. Freeze early layers (capturing edges and textures), retrain top layers to distinguish between dogs and cats (new dataset). This approach accelerates training and enhances accuracy, even with limited data.
In this blog, I’ve detailed how transfer learning in machine learning enables the leveraging of pre-trained models to simplify the learning of a new task, especially when training data is scarce.
I've covered various types, including inductive, transductive, unsupervised, as well as domain adaptation, confusion, one-shot, and zero-shot variants. I explored real‑world use in computer vision, natural language processing, medical imaging, object detection, and sentiment analysis.
Next steps:
Pick a suitable foundation model for your domain (e.g., ResNet or BERT).
Experiment with using it as a fixed feature extractor vs. fine‑tuning.
Evaluate model performance with limited target data.
Explore advanced cases, such as one-shot learning or domain confusion.
Share your findings or connect with experts—like on LinkedIn, where many practitioners post hands‑on transfer learning stories and tutorials.
By following this approach, you can accelerate model development with improved performance while conserving data and computational resources, especially for projects with limited labeled data.
