Lottery ticket hypothesis
In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain a subnetwork which can be tuned to a similar performance as tuning the whole network.
The original statement of the hypothesis is:
A randomly-initialized, dense neural network contains a subnetwork that is initialized such that—when trained in isolation—it can match the test accuracy of the original network after training for at most the same number of iterations.The original algorithm is:
- Randomily initialize dense network: weights.
- Train for iterations → weights.
- Magnitude pruning: build a bitmask by setting to 0 the lowest-magnitude weights in each layer, or repeat train → prune across rounds to reach higher sparsity.
- Reset surviving weights to initialization: use as the starting point. Here is elementwise multiplication.
- Train the masked network with the same optimizer, schedule, and data.
- Declare "winning ticket" if it achieves comparable test accuracy in iterations.
Such networks are a priori difficult to find, since before training, one does not know which of the exponentially many subnetwork would be "lottery tickets". However, after training, these lottery tickets can be discovered by the pruning algorithm.
It was found that during the original training of the initial dense network, the weights that would end up in the winning ticket change a lot, but the other weights change little.
It was found that if instead of, they re-sampled a different random initialization, and used instead, the trained would perform much worse. This indicates that what makes a ticket winning is both its connection graph and the precise initial weights of the connections.