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license: apache-2.0 |
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reproducing: "Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness" (https://arxiv.org/abs/2408.05446) |
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source code and usage examples: https://github.com/ETH-DISCO/self-ensembling |
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architecture based on Torchvision's Resnet152 default implementation |
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hyperparameters: |
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- criterion: `torch.nn.CrossEntropyLoss()` |
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- optimizer: `torch.optim.AdamW` |
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- scaler: `GradScaler` |
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- datasets: `["cifar10", "cirfar100"]` |
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- lr: `0.0001` |
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- num_epochs: `16` (higher would be even better, but maybe by <1%) |
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- crossmax_k: `2` (difference between `crossmax_k=2` and `crossmax_k=3` is about 1-2%, so it's not a big deal) |
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