sueszli/self-ensembling-resnet152

reproducing: "Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness" (https://arxiv.org/abs/2408.05446)

source code and usage examples: https://github.com/ETH-DISCO/self-ensembling

architecture based on Torchvision's Resnet152 default implementation

hyperparameters:

• criterion: torch.nn.CrossEntropyLoss()
• optimizer: torch.optim.AdamW
• scaler: GradScaler
• datasets: ["cifar10", "cirfar100"]
• lr: 0.0001
• num_epochs: 16 (higher would be even better, but maybe by <1%)
• crossmax_k: 2 (difference between crossmax_k=2 and crossmax_k=3 is about 1-2%, so it's not a big deal)