Magnificent Minified Models
Rich Harang Hillary Sanders
This paper concerns itself with the task of taking a large trained neural
network and 'compressing' it to be smaller by deleting parameters or entire
neurons, with minimal decreases in the resulting model accuracy. We compare
various methods of parameter and neuron selection: dropout-based neuron damage
estimation, neuron merging, absolute-value based selection, random selection,
OBD (Optimal Brain Damage). We also compare a variation on the classic OBD
method that slightly outperformed all other parameter and neuron selection
methods in our tests with substantial pruning, which we call OBD-SD. We compare
these methods against quantization of parameters. We also compare these
techniques (all applied to a trained neural network), with neural networks
trained from scratch (random weight initialization) on various pruned
architectures. Our results are only barely consistent with the Lottery Ticket
Hypothesis, in that fine-tuning a parameter-pruned model does slightly better
than retraining a similarly pruned model from scratch with randomly initialized
weights. For neuron-level pruning, retraining from scratch did much better in
our experiments.