diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py
index 2087a38..ccbd96a 100644
--- a/face_swapper/src/models/loss.py
+++ b/face_swapper/src/models/loss.py
@@ -54,7 +54,7 @@ class CycleLoss(nn.Module):
 		super().__init__()
 		self.config_batch_size = config_parser.getint('training.loader', 'batch_size')
 		self.config_cycle_weight = config_parser.getfloat('training.losses', 'cycle_weight')
-		self.l1_loss = nn.L1Loss()
+		self.mae_loss = nn.L1Loss()
 
 	def forward(self, target_tensor : Tensor, cycle_tensor : Tensor, target_features : Tuple[Feature, ...], cycle_features : Tuple[Feature, ...]) -> Tuple[Loss, Loss]:
 		temp_tensors = []
@@ -63,9 +63,9 @@ class CycleLoss(nn.Module):
 			temp_tensor = torch.mean(torch.pow(output_feature - target_feature, 2).reshape(self.config_batch_size, -1), dim = 1).mean()
 			temp_tensors.append(temp_tensor)
 
-		cycle_feature_loss = torch.stack(temp_tensors).mean()
-		cycle_l1_loss = self.l1_loss(target_tensor, cycle_tensor)
-		cycle_loss = (cycle_feature_loss + cycle_l1_loss) * 0.5
+		feature_loss = torch.stack(temp_tensors).mean()
+		mae_loss = self.mae_loss(target_tensor, cycle_tensor)
+		cycle_loss = (feature_loss + mae_loss) * 0.5
 		weighted_feature_loss = cycle_loss * self.config_cycle_weight
 		return cycle_loss, weighted_feature_loss