Knowledge distillation, where the output logits (soft targets) of a teacher model are used as the desired output for training a student model, offers several advantages over traditional training methods that use hard labels. Here's why knowledge distillation can be more effective:
- Logits from the teacher model contain more information about the data than hard labels. They indicate not just the most likely class, but also provide insights into other possible classes, encoding nuanced information about class similarities.
- Using soft targets acts as a form of regularization, helping the student model to generalize better by smoothing the decision boundaries and learning to output probabilities similar to the teacher's.
- Soft targets reveal inter-class relationships, indicating how similar or different the classes are according to the teacher's understanding, helping the student model to learn a richer representation of the data.
- The teacher model's implicit knowledge, gained through its training process, is transferred to the student. This includes understanding complex patterns and relationships in the data, beyond what is captured by the hard labels.
- Knowledge distillation allows for the student model to have a different, potentially more lightweight architecture, making it suitable for deployment in resource-constrained environments while retaining much of the performance.
- Soft probabilities from the teacher can provide a cleaner training signal than potentially noisy original labels, averaging out some of the noise and offering the student a clearer direction for learning.
In essence, knowledge distillation leverages the comprehensive understanding encapsulated in the teacher model's predictions, providing a richer training signal to the student model than hard labels. This method helps the student model to learn both the explicit task and the underlying data structure more effectively.