Pavel Izmailov, Andrew Wilson, Dmitry Vetrov, Timur Garipov, Dmitrii Podoprikhin
We lifted 9 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| timgaripov/swa | canonical | 2 of 2 |
| dice-group/aswa | pwc_unofficial | 3 of 6 |
| zlwangustc/SWA_paddle | pwc_unofficial | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| adjust_learning_rate | Ran | dice-group/aswa/utils.py code served (permissive licence) · get_code("14ebf34e2f003912") |
| check_bn | Ran | dice-group/aswa/utils.py code served (permissive licence) · get_code("6e59364a7cf6008f") |
| conv3x3 | Ran | timgaripov/swa/models/wide_resnet.py code served (permissive licence) · get_code("00e569acd6b45ef0") |
| conv3x3 | Ran | timgaripov/swa/models/preresnet.py code served (permissive licence) · get_code("583f9780bdd00a45") |
| make_layers | Ran | dice-group/aswa/models/vgg.py code served (permissive licence) · get_code("4a29d37fffcfd8b1") |
| adjust_learning_rate | Not yet run | zlwangustc/SWA_paddle/utils.py code served (permissive licence) · get_code("8d543b4110270e15") |
| eval | Not yet run | dice-group/aswa/utils.py code served (permissive licence) · get_code("ff618422d0032e4e") |
| schedule | Not yet run | dice-group/aswa/ddp_train.py code served (permissive licence) · get_code("24584656ffcaea3e") |
| selected_epochs | Not yet run | dice-group/aswa/analysis.py code served (permissive licence) · get_code("fd2f0d7779c8d75b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple averaging of multiple points along the trajectory of SGD, with a cyclical or constant learning rate, leads to better generalization than conventional training. We also show that this Stochastic Weight Averaging (SWA) procedure finds much flatter solutions than SGD, and approximates the recent Fast Geometric Ensembling (FGE) approach with a single model. Using SWA we achieve notable improvement in test accuracy over conventional SGD training on a range of state-of-the-art residual networks, PyramidNets, DenseNets, and Shake-Shake networks on CIFAR-10, CIFAR-100, and ImageNet. In short, SWA is extremely easy to implement, improves generalization, and has almost no computational overhead.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1803.05407")
get_code_for_paper("1803.05407")
have("1803.05407")
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