What is ReZero?
ReZero is a normalization approach used in machine learning that dynamically facilitates well-behaved gradients and arbitrarily deep signal propagation. The goal of ReZero is to simplify the training process while still providing high-quality results.
How Does ReZero Work?
The ReZero approach initializes each layer to perform the identity operation. For each layer, a residual connection is introduced for the input signal $x$ and one trainable parameter $\alpha$ that modulates the non-trivial transformation of a layer $F(\mathbf{x})$. The formula for ReZero looks like:
$$ \mathbf{x}\_{i+1}=\mathbf{x}\_{i}+\alpha_{i} F\left(\mathbf{x}\_{i}\right) $$
At the beginning of training, $\alpha=0$. So, the gradients for all parameters defining $F$ vanish initially. However, they dynamically evolve to suitable values during the initial stages of training. The architecture is illustrated in the figure:
Benefits of ReZero
The benefits of ReZero are that it allows for the training of deeper neural networks without adding any extra normalization layers, and thus reduces computational complexity. Additionally, ReZero makes the gradient more transparent and it allows for greater stacking of ResNet blocks, improving model accuracy.
ReZero in Action
ReZero has applications in several fields, such as natural language processing, computer vision, and speech recognition. It has shown to be especially effective in image segmentation and classification tasks, signal processing, and sequence prediction.
In Conclusion
ReZero is an effective normalization approach in machine learning that allows for deeper neural network training without adding extra computational complexity. It dynamically evolves gradients to suitable values during initial training stages, making it a useful tool in natural language processing, computer vision, and speech recognition, among other fields.
