@YuShen1116

*May 2019*

tl;dr: The author states the lottery ticket hypothesis: A randomly-initialized, dense neural network contains a
sub-network that is initialized such that – when trained in isolation – it can match the est accuracy of the original
network after training for at most the same number of iterations. This could be a good approach to compress the NN
without harming performance too much.

#### Overall impression

By using the pruning method of this paper, the winning tickets(pruned network) are 10-20% (or less) of the size of the
original network. Down to that size, those networks meet or exceed the original network’s test accuracy in at most the
same number of iterations.

#### Key ideas

- Summaries of the key ideas
- Usual pruning method
- Randomly initialize a neural network.
- Train the network for j iterations, arriving at parameters \theta_j.
- Prune p% of the parameters from \theta_j.
- Reset the remaining parameters and re-train the pruned-model(winning ticket)

- Paper’s pruning method
- Above pruning is one-shot, the author focus on iterative pruning,
which repeatedly trains, prunes, and resets the network over n rounds.
- each round prunes p^(1/n)% of the parameters.
- Other steps are the same
- Re-initialization after pruning would destroy the performance.

#### Technical details

- DO NOT re-initialize the model after pruning.
- This pruning method is sensitive to learning rate. It requires warm up(increase learning rate by step) to find winning tickets
at higher learning rates.

#### Notes

- It would be a good approach to try this iterative prune method.
However, this paper is applying a complex model(VGG, Resnet) on simple dataset(cifar-10, mnist),
it’s really hard to say the real performance of the pruning.
- The reason for warm up is still un-certain