diff --git a/monai/networks/blocks/dynunet_block.py b/monai/networks/blocks/dynunet_block.py
index bb654d841c..e637b53212 100644
--- a/monai/networks/blocks/dynunet_block.py
+++ b/monai/networks/blocks/dynunet_block.py
@@ -32,7 +32,9 @@ class UnetResBlock(nn.Module):
         out_channels: number of output channels.
         kernel_size: convolution kernel size.
         stride: convolution stride.
-        norm_name: feature normalization type and arguments.
+        norm_name: feature normalization type and arguments. If you need to use `"group"` or `"instance"` and want to
+            maintain the consistency with previous version v0.5.3, please set `affine=True`. For example:
+            `norm_name=("instance", {"affine": True})`.
 
     """
 
@@ -106,7 +108,9 @@ class UnetBasicBlock(nn.Module):
         out_channels: number of output channels.
         kernel_size: convolution kernel size.
         stride: convolution stride.
-        norm_name: feature normalization type and arguments.
+        norm_name: feature normalization type and arguments. If you need to use `"group"` or `"instance"` and want to
+            maintain the consistency with previous version v0.5.3, please set `affine=True`. For example:
+            `norm_name=("instance", {"affine": True})`.
 
     """
 
@@ -163,7 +167,9 @@ class UnetUpBlock(nn.Module):
         kernel_size: convolution kernel size.
         stride: convolution stride.
         upsample_kernel_size: convolution kernel size for transposed convolution layers.
-        norm_name: feature normalization type and arguments.
+        norm_name: feature normalization type and arguments. If you need to use `"group"` or `"instance"` and want to
+            maintain the consistency with previous version v0.5.3, please set `affine=True`. For example:
+            `norm_name=("instance", {"affine": True})`.
 
     """
 
diff --git a/monai/networks/nets/dynunet.py b/monai/networks/nets/dynunet.py
index 4af70b22c7..e9bf03a64c 100644
--- a/monai/networks/nets/dynunet.py
+++ b/monai/networks/nets/dynunet.py
@@ -86,7 +86,7 @@ class DynUNet(nn.Module):
         strides: convolution strides for each blocks.
         upsample_kernel_size: convolution kernel size for transposed convolution layers. The values should
             equal to strides[1:].
-        norm_name: feature normalization type and arguments. Defaults to ``INSTANCE``.
+        norm_name: feature normalization type and arguments. Defaults to ``("INSTANCE", {"affine": True})``.
         deep_supervision: whether to add deep supervision head before output. Defaults to ``False``.
             If ``True``, in training mode, the forward function will output not only the last feature
             map, but also the previous feature maps that come from the intermediate up sample layers.