What does "Gradient Norms" mean?
Table of Contents
Gradient norms are a way to measure how much change a model's output will have based on small changes in its input. Essentially, they tell us how sensitive a model is to different inputs during its training process.
Importance in Training
During training, models adjust their parameters to improve accuracy. Gradient norms help identify which parts of the training data are most influential in this process. When a model sees input data, the gradient norms show how strongly that input affects the model's learning.
Measuring Influence
In some scenarios, like with diffusion models, gradient norms can vary based on the order in which data is processed. This can make it harder to figure out which training samples impacted the model's decisions the most. Understanding these variations helps in refining how we assess a model's learning and influences.
Conclusion
Overall, gradient norms play a key role in understanding and improving models by highlighting the relationship between training data and the model's ability to produce accurate outputs.