First published: 2018/03/09 (5 years ago) Abstract: Neural network pruning techniques can reduce the parameter counts of trained
networks by over 90%, decreasing storage requirements and improving
computational performance of inference without compromising accuracy. However,
contemporary experience is that the sparse architectures produced by pruning
are difficult to train from the start, which would similarly improve training
We find that a standard pruning technique naturally uncovers subnetworks
whose initializations made them capable of training effectively. Based on these
results, we articulate the "lottery ticket hypothesis:" dense,
randomly-initialized, feed-forward networks contain subnetworks ("winning
tickets") that - when trained in isolation - reach test accuracy comparable to
the original network in a similar number of iterations. The winning tickets we
find have won the initialization lottery: their connections have initial
weights that make training particularly effective.
We present an algorithm to identify winning tickets and a series of
experiments that support the lottery ticket hypothesis and the importance of
these fortuitous initializations. We consistently find winning tickets that are
less than 10-20% of the size of several fully-connected and convolutional
feed-forward architectures for MNIST and CIFAR10. Above this size, the winning
tickets that we find learn faster than the original network and reach higher
Frankle and Carbin discover so-called winning tickets, subset of weights of a neural network that are sufficient to obtain state-of-the-art accuracy. The lottery hypothesis states that dense networks contain subnetworks – the winning tickets – that can reach the same accuracy when trained in isolation, from scratch. The key insight is that these subnetworks seem to have received optimal initialization. Then, given a complex trained network for, e.g., Cifar, weights are pruned based on their absolute value – i.e., weights with small absolute value are pruned first. The remaining network is trained from scratch using the original initialization and reaches competitive performance using less than 10% of the original weights. As soon as the subnetwork is re-initialized, these results cannot be reproduced though. This suggests that these subnetworks obtained some sort of “optimal” initialization for learning.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).