diff --git a/timm/models/efficientnet_blocks.py b/timm/models/efficientnet_blocks.py
index 114533cf..e81bb75b 100644
--- a/timm/models/efficientnet_blocks.py
+++ b/timm/models/efficientnet_blocks.py
@@ -218,12 +218,13 @@ class InvertedResidual(nn.Module):
                  stride=1, dilation=1, pad_type='', act_layer=nn.ReLU, noskip=False,
                  exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1,
                  se_ratio=0., se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None,
-                 conv_kwargs=None, drop_path_rate=0.):
+                 conv_kwargs=None, drop_path_rate=0., aa_layer=None):
         super(InvertedResidual, self).__init__()
         norm_kwargs = norm_kwargs or {}
         conv_kwargs = conv_kwargs or {}
         mid_chs = make_divisible(in_chs * exp_ratio)
         has_se = se_ratio is not None and se_ratio > 0.
+        use_aa = aa_layer is not None and stride == 2
         self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
         self.drop_path_rate = drop_path_rate
 
@@ -234,10 +235,11 @@ class InvertedResidual(nn.Module):
 
         # Depth-wise convolution
         self.conv_dw = create_conv2d(
-            mid_chs, mid_chs, dw_kernel_size, stride=stride, dilation=dilation,
+            mid_chs, mid_chs, dw_kernel_size, stride=1 if use_aa else stride, dilation=dilation,
             padding=pad_type, depthwise=True, **conv_kwargs)
         self.bn2 = norm_layer(mid_chs, **norm_kwargs)
         self.act2 = act_layer(inplace=True)
+        self.aa = aa_layer(mid_chs, stride=stride) if use_aa else None
 
         # Squeeze-and-excitation
         if has_se:
@@ -269,6 +271,8 @@ class InvertedResidual(nn.Module):
         x = self.conv_dw(x)
         x = self.bn2(x)
         x = self.act2(x)
+        if self.aa is not None:
+            x = self.aa(x)
 
         # Squeeze-and-excitation
         if self.se is not None:
diff --git a/timm/models/efficientnet_builder.py b/timm/models/efficientnet_builder.py
index f670aa6c..bb8ce796 100644
--- a/timm/models/efficientnet_builder.py
+++ b/timm/models/efficientnet_builder.py
@@ -221,7 +221,7 @@ class EfficientNetBuilder:
     """
     def __init__(self, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
                  output_stride=32, pad_type='', act_layer=None, se_kwargs=None,
-                 norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_path_rate=0., feature_location='',
+                 norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_path_rate=0., aa_layer=None, feature_location='',
                  verbose=False):
         self.channel_multiplier = channel_multiplier
         self.channel_divisor = channel_divisor
@@ -233,6 +233,7 @@ class EfficientNetBuilder:
         self.norm_layer = norm_layer
         self.norm_kwargs = norm_kwargs
         self.drop_path_rate = drop_path_rate
+        self.aa_layer = aa_layer
         if feature_location == 'depthwise':
             # old 'depthwise' mode renamed 'expansion' to match TF impl, old expansion mode didn't make sense
             _logger.warning("feature_location=='depthwise' is deprecated, using 'expansion'")
@@ -269,6 +270,8 @@ class EfficientNetBuilder:
             if ba.get('num_experts', 0) > 0:
                 block = CondConvResidual(**ba)
             else:
+                # FIXME: `aa_layer` only impl for `InvertedResidual`. Add `CondConvResidual`?
+                ba['aa_layer'] = self.aa_layer
                 block = InvertedResidual(**ba)
         elif bt == 'ds' or bt == 'dsa':
             ba['drop_path_rate'] = drop_path_rate
diff --git a/timm/models/mobilenetv3.py b/timm/models/mobilenetv3.py
index 543b33ea..ce59c5b5 100644
--- a/timm/models/mobilenetv3.py
+++ b/timm/models/mobilenetv3.py
@@ -18,7 +18,7 @@ from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_la
 from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
 from .features import FeatureInfo, FeatureHooks
 from .helpers import build_model_with_cfg, default_cfg_for_features
-from .layers import SelectAdaptivePool2d, Linear, create_conv2d, get_act_fn, hard_sigmoid
+from .layers import SelectAdaptivePool2d, Linear, BlurPool2d, create_conv2d, get_act_fn, hard_sigmoid
 from .registry import register_model
 
 __all__ = ['MobileNetV3']
@@ -85,7 +85,7 @@ class MobileNetV3(nn.Module):
 
     def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_features=1280, head_bias=True,
                  channel_multiplier=1.0, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
-                 se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg'):
+                 se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg', aa_layer=None):
         super(MobileNetV3, self).__init__()
 
         self.num_classes = num_classes
@@ -101,7 +101,7 @@ class MobileNetV3(nn.Module):
         # Middle stages (IR/ER/DS Blocks)
         builder = EfficientNetBuilder(
             channel_multiplier, 8, None, 32, pad_type, act_layer, se_kwargs,
-            norm_layer, norm_kwargs, drop_path_rate, verbose=_DEBUG)
+            norm_layer, norm_kwargs, drop_path_rate, aa_layer, verbose=_DEBUG)
         self.blocks = nn.Sequential(*builder(stem_size, block_args))
         self.feature_info = builder.features
         head_chs = builder.in_chs
@@ -160,7 +160,7 @@ class MobileNetV3Features(nn.Module):
     def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck',
                  in_chans=3, stem_size=16, channel_multiplier=1.0, output_stride=32, pad_type='',
                  act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0., se_kwargs=None,
-                 norm_layer=nn.BatchNorm2d, norm_kwargs=None):
+                 norm_layer=nn.BatchNorm2d, norm_kwargs=None, aa_layer=None,):
         super(MobileNetV3Features, self).__init__()
         norm_kwargs = norm_kwargs or {}
         self.drop_rate = drop_rate
@@ -174,7 +174,7 @@ class MobileNetV3Features(nn.Module):
         # Middle stages (IR/ER/DS Blocks)
         builder = EfficientNetBuilder(
             channel_multiplier, 8, None, output_stride, pad_type, act_layer, se_kwargs,
-            norm_layer, norm_kwargs, drop_path_rate, feature_location=feature_location, verbose=_DEBUG)
+            norm_layer, norm_kwargs, drop_path_rate, aa_layer, feature_location=feature_location, verbose=_DEBUG)
         self.blocks = nn.Sequential(*builder(stem_size, block_args))
         self.feature_info = FeatureInfo(builder.features, out_indices)
         self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices}
@@ -405,6 +405,20 @@ def mobilenetv3_small_100(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def mobilenetv3_large_075_aa(pretrained=False, aa_layer=BlurPool2d, **kwargs):
+    """ MobileNet V3 """
+    model = _gen_mobilenet_v3('mobilenetv3_large_075', 1.0, pretrained=pretrained, aa_layer=aa_layer, **kwargs)
+    return model
+
+
+@register_model
+def mobilenetv3_large_100_aa(pretrained=False, aa_layer=BlurPool2d, **kwargs):
+    """ MobileNet V3 """
+    model = _gen_mobilenet_v3('mobilenetv3_large_100', 1.0, pretrained=pretrained, aa_layer=aa_layer, **kwargs)
+    return model
+
+
 @register_model
 def mobilenetv3_rw(pretrained=False, **kwargs):
     """ MobileNet V3 """