However, many of the existing pruning methods rely on classic regularizers that act on the individual weights by penalizing them to zero (Guo et al., 2019;Sanh et al., 2020).
the pruned model tends to maintain the same architecture as the original model despite the reduced parameter count, which does not practically lead to an improvement in inference latency (Wen et al., 2016).