Transfer learning is a machine learning technique where knowledge gained from one task is used to improve performance on a related task. It enables a model to begin with prior experience, reducing the amount of data and training time required. 

Tools such as TensorFlow Hub and NVIDIA TAO Toolkit support transfer learning in practical settings like diagnosing health conditions, reviewing financial records, or organizing retail product information.

It forms part of supervised learning, where models are trained on labeled examples that match inputs to known outcomes. In typical scenarios, each new task requires a separate training cycle. Transfer learning builds on previous training by allowing the model to retain and apply relevant features, leading to more efficient adaptation when solving new problems. 

How does transfer learning work? 

Transfer learning reuses knowledge from one task to improve learning in another. While the exact approach can vary, the process typically involves selecting a pre-trained model, adapting it to a new context, and updating it using data from the new task.

Choosing a relevant source model

The process begins by selecting a model trained on a task with features similar to the one being addressed. For example, a healthcare model trained to classify medical scans may serve as the foundation for detecting a different type of condition.

Deciding what to reuse and adapt

Next, teams identify which parts of the model to keep. Early layers — stages of the model that learn to detect basic patterns, such as edges or shapes — often contain general knowledge that can be transferred. Later layers, which learn more task-specific outputs, may be replaced or updated.

Adjusting model architecture or inputs

The model’s structure, known as its architecture, may need to be adapted. Input formats (such as image dimensions or file structure) and output types (such as categories or labels) may differ between tasks. Changes can include resizing the input data or adding new processing steps so the model can interpret and respond to the new information correctly.

Training the model on task-specific data

Once adapted, the model is trained using examples from the new task. This often involves fine-tuning — updating selected parts of the model using a smaller dataset — to help it learn patterns relevant to the new domain. In retail, this could mean using updated product listings to adjust a model originally trained on general images.

Evaluating outcomes and refining the approach

After training, performance is tested using examples that reflect the new use case. Further adjustments may be needed to improve accuracy. In finance, for instance, the model might be refined to detect irregular transactions without raising unnecessary alerts.

Transfer learning use cases 

Transfer learning is widely used in domains where labeled data is limited or where tasks share structural similarities. IDC reports that 46% of IT leaders use AI to assess skills and personalize training, with models often adapted to specific domains. 

By adapting pre-trained models, teams can reduce development time and improve performance in specialized applications.

Medical image analysis

In healthcare, transfer learning supports the analysis of medical images such as X-rays or MRI scans. A model trained on general image recognition tasks can be adapted to detect specific conditions, such as tumors or fractures. Because medical datasets are often small and sensitive, reusing a model that already understands visual features helps improve accuracy without needing large volumes of labelled data.

Customer behavior modeling

Retail and eCommerce teams use transfer learning to predict customer actions based on previous interactions. A model originally trained on browsing patterns can be fine-tuned to identify when a customer is likely to return, churn, or convert. Reusing knowledge from broader datasets allows businesses to personalize experiences using fewer real-time signals.

Financial risk detection

In finance, transfer learning helps detect anomalies or assess credit risk. A model trained on past transaction data can be updated to identify emerging fraud patterns or changes in repayment behavior. Because financial trends shift over time, adapting an existing model is more effective than starting from zero for each new risk scenario.

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