Delete vanilla_at.py

src/defenses/vanilla_at.py

-import torch
-import torch.nn.functional as F
-from .base_defense import BaseDefense
-
-# VanillaAdversarialTrainingDefense class implements vanilla adversarial training
-class VanillaAdversarialTrainingDefense(BaseDefense):
-    def __init__(self, student_model, teacher_model=None, adv_lambda=1.0):
-        super().__init__(student_model, teacher_model)  # Initialize base class
-        self.adv_lambda = adv_lambda                    # Weight for adversarial loss
-
-    # Loss function combining clean and adversarial cross-entropy loss
-    def loss_function(self, x, y, x_adv=None, **kwargs):
-        if x_adv is None:
-            x_adv = x                              # If no adversarial examples, use clean examples
-
-        logits_adv = self.student_model(x_adv)     # Forward pass for adversarial input
-        loss_adv = F.cross_entropy(logits_adv, y)  # Cross-entropy loss for adversarial input
-
-        if self.adv_lambda < 1.0:
-            logits_clean = self.student_model(x)                                      # Forward pass for clean input
-            loss_clean = F.cross_entropy(logits_clean, y)                             # Cross-entropy loss for clean input
-            loss = self.adv_lambda * loss_adv + (1.0 - self.adv_lambda) * loss_clean  # Weighted loss
-        else:
-            loss = loss_adv
-
-        return loss