Gradient Clipping
A technique used to mitigate the exploding gradient problem during the training of neural networks by capping gradients to a specified value range.
Gradient clipping is crucial for stabilizing the training of deep neural networks, especially in situations where the gradients can grow exponentially large and cause undesirable large parameter updates, ultimately leading to failed convergence or NaNs in calculations. It is implemented by setting a threshold for the gradients; if any gradient exceeds this threshold, it is scaled down to a manageable level. This process ensures that the updates being applied during backpropagation are controlled in magnitude, particularly beneficial in recurrent neural networks (RNNs) and other deep models prone to such instability. By maintaining the balance of the learning process, gradient clipping aids the model in achieving better convergence properties, enhancing the ability to learn complex patterns without deviation due to overly large or small gradient steps.
The concept of gradient clipping first emerged in the late 1990s, but it gained notable popularity and widespread adoption in the AI community around 2013 as deep learning models advanced and the challenges of training them became evident.
Gradient clipping was popularized by various researchers working on RNNs and deep learning stability, with early acknowledgments often pointing to the works of Sepp Hochreiter and Yoshua Bengio, whose foundational work on deep learning’s computational characteristics underscored the need for such methods.