diff --git a/torchvision/models/video/resnet.py b/torchvision/models/video/resnet.py
index a9e59a149c0..caa32505ab4 100644
--- a/torchvision/models/video/resnet.py
+++ b/torchvision/models/video/resnet.py
@@ -79,6 +79,55 @@ def get_downsample_stride(stride):
         return (1, stride, stride)
 
 
+class Conv3DDepthwise(nn.Conv3d):
+    """Depthwise version of the 3D conv,
+    used for implementing channel-separated networks.
+    """
+    def __init__(self,
+                 in_planes,
+                 out_planes,
+                 stride=1,
+                 padding=1):
+
+        assert in_planes == out_planes
+        super(Conv3DDepthwise, self).__init__(
+            in_channels=in_planes,
+            out_channels=out_planes,
+            kernel_size=(3, 3, 3),
+            stride=stride,
+            padding=padding,
+            groups=in_planes,
+            bias=False)
+
+    @staticmethod
+    def get_downsample_stride(stride):
+        return (stride, stride, stride)
+
+
+class IPConv3DDepthwise(nn.Sequential):
+    """Depthwise version of the 3D conv,
+    used for implementing interaction-preserving
+    channel-separated networks.
+    """
+    def __init__(self,
+                 in_planes,
+                 out_planes,
+                 stride=1,
+                 padding=1):
+
+        assert in_planes == out_planes
+        super(IPConv3DDepthwise, self).__init__(
+            nn.Conv3d(in_planes, out_planes, kernel_size=1, bias=False),
+            nn.BatchNorm3d(out_planes),
+            nn.ReLU(inplace=True),
+            Conv3DDepthwise(out_planes, out_planes, None, stride)
+        )
+
+    @staticmethod
+    def get_downsample_stride(stride):
+        return (stride, stride, stride)
+
+
 class BasicBlock(nn.Module):
 
     expansion = 1
@@ -339,3 +388,45 @@ def r2plus1d_18(pretrained=False, progress=True, **kwargs):
                          conv_makers=[Conv2Plus1D] * 4,
                          layers=[2, 2, 2, 2],
                          stem=R2Plus1dStem, **kwargs)
+
+
+def ir_csn_152(pretrained=False, progress=False, **kwargs):
+    """Constructor for the 152 layer deep ir-CSN network as described
+    in https://arxiv.org/abs/1904.02811.
+    Note that video model zoo (https://github.com/facebookresearch/VMZ) provides 
+    models pretrained on large scale benchmarks such as Sports1M and URU.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Kinetics-400
+        progress (bool): If True, displays a progress bar of the download to stderr
+
+    Returns:
+        nn.Module: ir-CSN-152 network
+    """
+    return _video_resnet('ir_csn_152',
+                         False, False,
+                         block=Bottleneck,
+                         conv_makers=[Conv3DDepthwise] * 4,
+                         layers=[3, 8, 36, 3],
+                         stem=BasicStem, **kwargs)
+
+
+def ip_csn_152(pretrained=False, progress=False, **kwargs):
+    """Constructor for the 152 layer deep ip-CSN network as described
+    in https://arxiv.org/abs/1904.02811.
+    Note that video model zoo (https://github.com/facebookresearch/VMZ) provides 
+    models pretrained on large scale benchmarks such as Sports1M and URU.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Kinetics-400
+        progress (bool): If True, displays a progress bar of the download to stderr
+
+    Returns:
+        nn.Module: ip-CSN-152 network
+    """
+    return _video_resnet('ip_csn_152',
+                         False, False,
+                         block=Bottleneck,
+                         conv_makers=[IPConv3DDepthwise] * 4,
+                         layers=[3, 8, 36, 3],
+                         stem=BasicStem, **kwargs)