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 performance. We find that a standard technique for pruning weights 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-arrive at comparable test accuracy in a comparable 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. Furthermore, the winning tickets we find above that size learn faster than the original network and exhibit higher test accuracy. arXiv:1803.03635v4 [cs.LG] 27 Nov 2018 1. Randomly initialize a neural network f (x; θ 0 ) (where θ 0 ∼ D θ ). 2. Train the network for j iterations, reaching parameters θ j . 3. Prune s% of the parameters, creating a mask m where P m = (100 − s)%. 4. To extract the winning ticket, reset the remaining parameters to their values in θ 0 , creating the untrained network f (x; m θ 0 ). If dense networks contain winning tickets and pruning reveals them, then the network f (x; m θ 0 )when trained for j iterations-will reach similar accuracy to f (x; θ j ) at least as quickly, and m will be too sparse for a randomly-reinitialized or randomly-sparsified network to do the same. Results. We identify winning tickets in a fully-connected architecture for MNIST and convolutional architectures for CIFAR10 across several optimization strategies (SGD, momentum, and Adam) with techniques like dropout, weight decay, and batchnorm. In deeper networks, our pruning-based strategy for finding winning tickets is sensitive to the learning rate: it requires warmup to find winning tickets at higher learning rates. The winning tickets we find are 10-20% (or less) of the size of the
Recent work REF proposes The Lottery Ticket Hypothesis claiming that dense, randomly-initialized and feed-forward networks contain subnetworks that can be recognized and trained to get a comparable test accuracy to the original network.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
ICLR 2019
Mathematics Computer Science