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In order to preserve accuracy, after removing connections the network needs to be retrained. Compress a trained network will actually require another multiple rounds of training, that may take a long time and require customization to the training process (sparse connections, weight encoding). I'm thinking if we can directly compress the trained model statically, meaning that no need to retrain at all, while preserve as much precision of the original network as possible


If we can achieve about the same accuracy from a compressed network, why don't we adapt this approach for all kinds of networks? Why don't we train networks and then compress them once they are trained? Maybe someone more knowledgeable than I can answer this.


@aeu I think it takes time to retain the model, and it is tricky to do so as the model is represented in a different way now