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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Domain adaptation - Wikipedia View original
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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
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