7.4 Transfer learning and fine-tuning with pre-trained CNNs

Transfer learning revolutionizes deep learning by reusing knowledge from one task to boost performance on another. It's like borrowing a friend's expertise to ace a new challenge. This approach saves time, reduces computational needs, and shines with small datasets.

Pre-trained CNNs, like VGG and ResNet, are the secret sauce of transfer learning. These models, trained on massive datasets like ImageNet, can be tweaked for new tasks. It's like customizing a pro athlete's skills for your local sports team.

Understanding Transfer Learning and Pre-trained CNNs

Concept of transfer learning

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• Transfer learning reuses knowledge from one task to improve performance on another
• Reduced training time, lower computational requirements, improved performance on small datasets
• Feature extraction and fine-tuning types leverage pre-trained models
• Pre-trained models trained on large datasets (ImageNet) with common architectures (VGG, ResNet, Inception)

Adaptation of pre-trained CNNs

• Choose pre-trained model, remove final classification layer, add new layers for target task
• Freeze pre-trained layers (optional) to preserve learned features
• Domain and task adaptation techniques adjust model for new contexts
• Resize input images to match pre-trained model requirements, normalize input data

Fine-tuning and Performance Comparison

Process of fine-tuning

• Unfreeze some or all pre-trained layers, train on new dataset with lower learning rate
• Layer-wise fine-tuning and gradual unfreezing strategies optimize adaptation
• Hyperparameter tuning: learning rate selection, number of epochs, batch size optimization

Training from scratch vs transfer learning

• Training from scratch requires large datasets, longer training time, higher computational resources
• Transfer learning offers faster convergence, better performance on small datasets, lower overfitting risk
• Transfer learning excels with limited data, similar source/target domains
• Training from scratch preferred for large, diverse datasets, significantly different target tasks
• Evaluate using accuracy, training time, computational resources required