Papers › Rigging the Lottery: Making All Tickets Winners
Rigging the Lottery: Making All Tickets Winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen
Many applications require sparse neural networks due to space or inference time restrictions. There is a large body of work on training dense networks to yield sparse networks for inference, but this limits the size of the largest trainable sparse model to that of the largest trainable dense model. In this paper we introduce a method to train sparse neural networks with a fixed parameter count and a fixed computational cost throughout training, without sacrificing accuracy relative to existing dense-to-sparse training methods. Our method updates the topology of the sparse network during training by using parameter magnitudes and infrequent gradient calculations. We show that this approach requires fewer floating-point operations (FLOPs) to achieve a given level of accuracy compared to prior techniques. We demonstrate state-of-the-art sparse training results on a variety of networks and datasets, including ResNet-50, MobileNets on Imagenet-2012, and RNNs on WikiText-103. Finally, we provide some insights into why allowing the topology to change during the optimization can overcome local minima encountered when the topology remains static. Code used in our work can be found in github.com/google-research/rigl.
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Code
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Code Syntology ran Syntology
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3a0749baa452d466 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sparse Learning | ImageNet | Resnet-50: 80% Sparse | Top-1 Accuracy | 77.1 | #1 of 9 | Archive leaderboard | report |
| Sparse Learning | ImageNet | Resnet-50: 90% Sparse | Top-1 Accuracy | 76.4 | #2 of 9 | Archive leaderboard | report |
| Sparse Learning | ImageNet | MobileNet-v1: 75% Sparse | Top-1 Accuracy | 71.9 | #7 of 9 | Archive leaderboard | report |
| Sparse Learning | ImageNet | MobileNet-v1: 90% Sparse | Top-1 Accuracy | 68.1 | #8 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: RigL
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