I’m diving deep into model optimization and came across the concepts of overparameterization and reparameterization. I’ve read that both can lead to better model performance, but I’m a bit confused about how these techniques actually work to improve a model.

It doubles the parameters of the op during training, but then during inference you “reparameterize” which in this case means adding the weight/biases of the two branches together resulting in a single, mathematically identical conv op (same input, same output, one conv op instead of two summed branches).

Overparameterization gives a model more flexibility to capture complex patterns, which can improve its performance, even if it seems like it might overfit. Reparameterization, on the other hand, tweaks how parameters are represented to make training easier or more stable. Both methods can help a model learn better and perform well on new data by either boosting its learning capacity or optimizing the training process.

Overparameterization and reparameterization improve models by initially doubling the parameters during training, allowing for more flexibility and learning capacity. During inference, reparameterization combines the parameters of the two branches into one operation, resulting in a single, efficient convolutional operation that produces the same output as the combined branches.