Model distillation and pruning are two important techniques for compressing neural networks. They can reduce a model’s parameter count and computational complexity, improving its efficiency and performance. The following sections introduce both techniques.
Model distillation
Model distillation trains a smaller model to reproduce the predictions of a larger one. The larger model is called the “teacher,” and the smaller model the “student.” With more parameters and greater complexity, the teacher can generally fit the training and test data better. The student learns to imitate the teacher’s predictive behavior, aiming for similar performance in a smaller model.
Model distillation typically involves the following steps:
1. Train the teacher: Train a large deep neural network using conventional methods such as backpropagation and stochastic gradient descent, so that it performs well on the training data.
2. Generate soft labels: Use the teacher to make predictions on the training data and use its outputs as soft labels. Unlike conventional hard labels represented by one-hot encoding, soft labels provide more continuous information and better describe relationships between classes.
3. Train the student: Use the soft labels as training targets for a smaller deep neural network. The student takes the same kinds of inputs and produces the same kinds of outputs as the teacher, while using fewer parameters and a simpler architecture.
Distillation allows smaller models to retain performance while requiring less computation and storage.Soft labels also provide richer, more continuous information, helping the student learn relationships between classes. Distillation is widely used in natural language processing, computer vision, speech recognition and other fields.
Model pruning
Model pruning compresses a neural network by removing unnecessary neurons and connections. Neural networks often contain many parameters and redundant connections. Some contribute little to performance while substantially increasing computational complexity and storage requirements. Removing these parameters and connections can reduce model size and computation while retaining performance.
Model pruning typically involves the following steps:
1. Train the original model: Train a large deep neural network with conventional methods such as backpropagation and stochastic gradient descent, so that it performs well on the training data.
2. Assess neuron importance: Estimate each neuron’s contribution to the final output using methods such as L1 regularization, the Hessian matrix or Taylor expansion. Neurons with low importance can be treated as unnecessary.
3. Remove unnecessary neurons and connections: Prune neurons and connections according to their importance. This can be done by setting their weights to zero or removing the corresponding neurons and connections.
Pruning can reduce model size and computational complexity, improving efficiency.It can also help reduce overfitting and improve generalization. Like distillation, pruning is widely used in natural language processing, computer vision, speech recognition and other fields.
Although both techniques compress neural networks, their approaches and objectives differ. Distillation emphasizes teaching a student to imitate a teacher’s predictions; pruning emphasizes removing unnecessary parameters and connections.
