diff --git a/inverter_util.py b/inverter_util.py
index 0a58bb5..bce19c4 100644
--- a/inverter_util.py
+++ b/inverter_util.py
@@ -210,30 +210,6 @@ def silent_pass(self, m, in_tensor: torch.Tensor,
         # to store any specific data. Still useful for module tracking.
         pass
 
-    @staticmethod
-    def get_inv_max_pool_method(max_pool_instance):
-        """
-        Get dimension-specific max_pooling layer.
-        The forward pass and inversion are made in a
-        'dimensionality-agnostic' manner and are the same for
-        all nd instances of the layer, except for the functional
-        that needs to be used.
-
-        Args:
-            max_pool_instance: instance of max_pool layer.
-
-        Returns:
-            The correct functional used in the max_pooling layer.
-
-        """
-
-        conv_func_mapper = {
-            torch.nn.MaxPool1d: F.max_unpool1d,
-            torch.nn.MaxPool2d: F.max_unpool2d,
-            torch.nn.MaxPool3d: F.max_unpool3d
-        }
-        return conv_func_mapper[type(max_pool_instance)]
-
     def linear_inverse(self, m, relevance_in):
 
         if self.method == "e-rule":
@@ -347,18 +323,26 @@ def linear_fwd_hook(self, m, in_tensor: torch.Tensor,
         return
 
     def max_pool_nd_inverse(self, layer_instance, relevance_in):
-
         # In case the output had been reshaped for a linear layer,
         # make sure the relevance is put into the same shape as before.
         relevance_in = relevance_in.view(layer_instance.out_shape)
 
-        invert_pool = self.get_inv_max_pool_method(layer_instance)
-        inverted = invert_pool(relevance_in, layer_instance.indices,
-                               layer_instance.kernel_size, layer_instance.stride,
-                               layer_instance.padding, output_size=layer_instance.in_shape)
-        del layer_instance.indices
+        z = torch.zeros(layer_instance.in_shape)
+        # since scatter add will only work on a single dim, for MaxPool2d and MaxPool3d tensors need some flattening
+        is_2d_or_3d_maxpool = isinstance(layer_instance, torch.nn.MaxPool2d) or isinstance(layer_instance, torch.nn.MaxPool3d)
+        if is_2d_or_3d_maxpool:
+            z = z.flatten(2, len(layer_instance.in_shape)-1)
+            relevance_in = relevance_in.flatten(2, len(layer_instance.in_shape)-1)
+            indices = layer_instance.indices.reshape(relevance_in.shape)
+
+        relevance_out = z.scatter_add(2, indices, relevance_in)
+
+        # for MaxPool2d and MaxPool3d the correct shape has to be restored
+        if is_2d_or_3d_maxpool:
+            relevance_out.reshape(layer_instance.in_shape)
+
+        return relevance_out
 
-        return inverted
 
     @module_tracker
     def max_pool_nd_fwd_hook(self, m, in_tensor: torch.Tensor,