Transfer Learning

So far, you have learned how CNNs are structured and how architectures are designed. Now comes one of the most powerful ideas in modern Computer Vision: Transfer Learning.

Transfer learning is the reason why deep learning works even when you do not have millions of images.

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What Is Transfer Learning?

Transfer learning means:

Using knowledge learned from one task and applying it to another related task.

In Computer Vision, this usually means:

• Training a CNN on a very large dataset
• Reusing that trained model for a new problem

Instead of starting from scratch, you start from experience.

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Why Transfer Learning Works So Well

CNNs learn features in layers:

• Early layers learn edges and textures
• Middle layers learn shapes and patterns
• Deeper layers learn object-specific details

These early and middle features are useful for almost all vision tasks.

So instead of relearning edges every time, we reuse them.

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The Problem With Training From Scratch

Training a CNN from scratch requires:

• Huge datasets
• Large compute power
• Long training times

Most real-world projects do not have this luxury.

Transfer learning solves this problem.

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Pretrained Models Explained

A pretrained model is a CNN that has already been trained on a massive dataset.

The most famous dataset is:

• ImageNet (millions of labeled images)

Models trained on ImageNet have learned:

• Edges
• Textures
• Shapes
• Object structures

We reuse this learned knowledge.

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How Transfer Learning Is Applied

There are two common strategies:

Both are widely used, but in different situations.

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Strategy 1: Feature Extraction

In feature extraction:

• We keep the pretrained CNN fixed
• We remove the final classification layers
• We add our own classifier on top

The CNN acts as a feature extractor.

This approach is best when:

• Your dataset is small
• Your task is similar to the original task

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Strategy 2: Fine-Tuning

In fine-tuning:

• We start with a pretrained model
• We allow some layers to update during training

Fine-tuning lets the model adapt more deeply to your data.

This approach is best when:

• You have more data
• Your task is somewhat different

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Which Layers Should Be Fine-Tuned?

Not all layers should be retrained.

General rule:

• Freeze early layers (edges, textures)
• Fine-tune deeper layers (task-specific features)

This balances stability and adaptability.

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Transfer Learning Workflow

A typical workflow looks like this:

1. Choose a pretrained model
2. Load pretrained weights
3. Remove the final classification layer
4. Add a new task-specific head
5. Train with frozen or partially frozen layers

This workflow is used in most production CV systems.

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Why Transfer Learning Improves Performance

Transfer learning helps because:

• Models converge faster
• Overfitting is reduced
• Accuracy improves with less data

This is why it is the default approach today.

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Common Use Cases

Transfer learning is used in:

• Medical image analysis
• Face recognition
• Object detection
• Industrial inspection
• Autonomous vehicles

Almost every applied CV system uses it.

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Is This Coding or Theory?

This lesson is about decision-making and understanding.

You are learning:

• When to use transfer learning
• How to choose strategies
• Why it works

Actual implementation comes in upcoming lessons.

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Practice Questions

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Mini Design Exercise

Imagine you want to classify:

• X-ray images
• Traffic signs
• Product defects

Think:

• Would you freeze layers?
• Would you fine-tune?

This thinking is what real CV engineers do.

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Quick Recap

• Transfer learning reuses pretrained knowledge
• It reduces data and compute requirements
• Feature extraction and fine-tuning are core strategies
• It is the industry standard approach

Next lesson: Fine-Tuning CNNs — practical control over pretrained models.