CNN optimization strategies are crucial for achieving high-performance models in image processing tasks. Below, we discuss some of the key optimization techniques used in CNN training and inference.
Data Augmentation
Data augmentation is a powerful technique to increase the diversity of the training data. It helps prevent overfitting and improves the generalization of the model.
- Rotations
- Scaling
- Translation
- Flipping
Learning Rate Scheduling
The learning rate is a hyperparameter that controls how much we adjust the weights during training. Learning rate scheduling helps in tuning the learning rate during training to optimize the convergence.
- Step Decay
- Exponential Decay
- Plateau
- Cyclic Learning Rates
Regularization
Regularization techniques are used to prevent overfitting by penalizing large weights.
- L1 Regularization
- L2 Regularization
- Dropout
Batch Normalization
Batch normalization helps to normalize the inputs to a layer for each mini-batch, improving the stability of the learning process and acting as a form of regularization.
For further reading on CNN optimization, you might find the following tutorials helpful: