Preprint Open access
Neuron merging via inverse-activation regression for post-training compression of sigmoid neural networks
As neural networks continue to grow in scale, model compression is becoming increasingly important for efficient inference under limited computational resources. Structured pruning methods remove neurons or channels that are estimated to be less important, but the removed units may still contain useful information. Fro …