To incorporate the entropy-based importance metric into the loss function for a more efficient one-shot approach, we can explore differentiable proxies for the layer's entropy. While entropy itself is non-differentiable, we can consider approximations or surrogates that can be incorporated into the loss function. One approach could be to use a differentiable function that approximates the layer's entropy, such as the Shannon entropy or the Kullback-Leibler divergence. By including this differentiable proxy in the loss function, we can directly optimize for the reduction of layers with low entropy during training, leading to a more efficient one-shot approach. This would eliminate the need for iterative training and fine-tuning, streamlining the process and reducing computational costs.