diff --git a/kornia/augmentation/container/augment.py b/kornia/augmentation/container/augment.py
index 26ce74cdc9..914a1e29d4 100644
--- a/kornia/augmentation/container/augment.py
+++ b/kornia/augmentation/container/augment.py
@@ -175,8 +175,9 @@ class AugmentationSequential(TransformMatrixMinIn, ImageSequential):
         ... )
         >>> out = aug_list(input, mask, bbox)
 
-    How to use a dictionary as input with AugmentationSequential? The dictionary should starts with
-    one of the datakey availables.
+    How to use a dictionary as input with AugmentationSequential? The dictionary keys that start with
+    one of the available datakeys will be augmented accordingly. Otherwise, the dictionary item is passed
+    without any augmentation.
 
         >>> import kornia.augmentation as K
         >>> img = torch.randn(1, 3, 256, 256)
@@ -291,7 +292,7 @@ def inverse(  # type: ignore[override]
         """
         original_keys = None
         if len(args) == 1 and isinstance(args[0], dict):
-            original_keys, data_keys, args = self._preproc_dict_data(args[0])
+            original_keys, data_keys, args, invalid_data = self._preproc_dict_data(args[0])
 
         # args here should already be `DataType`
         # NOTE: how to right type to: unpacked args <-> tuple of args to unpack
@@ -324,7 +325,10 @@ def inverse(  # type: ignore[override]
         outputs = self._arguments_postproc(args, outputs, data_keys=self.transform_op.data_keys)  # type: ignore
 
         if isinstance(original_keys, tuple):
-            return {k: v for v, k in zip(outputs, original_keys)}
+            result = {k: v for v, k in zip(outputs, original_keys)}
+            if invalid_data:
+                result.update(invalid_data)
+            return result
 
         if len(outputs) == 1 and isinstance(outputs, list):
             return outputs[0]
@@ -414,7 +418,7 @@ def forward(  # type: ignore[override]
         # Unpack/handle dictionary args
         original_keys = None
         if len(args) == 1 and isinstance(args[0], dict):
-            original_keys, data_keys, args = self._preproc_dict_data(args[0])
+            original_keys, data_keys, args, invalid_data = self._preproc_dict_data(args[0])
 
         self.transform_op.data_keys = self.transform_op.preproc_datakeys(data_keys)
 
@@ -455,7 +459,10 @@ def forward(  # type: ignore[override]
         self._params = params
 
         if isinstance(original_keys, tuple):
-            return {k: v for v, k in zip(outputs, original_keys)}
+            result = {k: v for v, k in zip(outputs, original_keys)}
+            if invalid_data:
+                result.update(invalid_data)
+            return result
 
         if len(outputs) == 1 and isinstance(outputs, list):
             return outputs[0]
@@ -464,17 +471,19 @@ def forward(  # type: ignore[override]
 
     def _preproc_dict_data(
         self, data: Dict[str, DataType]
-    ) -> Tuple[Tuple[str, ...], List[DataKey], Tuple[DataType, ...]]:
+    ) -> Tuple[Tuple[str, ...], List[DataKey], Tuple[DataType, ...], Optional[Dict[str, Any]]]:
         if self.data_keys is not None:
             raise ValueError("If you are using a dictionary as input, the data_keys should be None.")
 
         keys = tuple(data.keys())
-        data_keys = self._read_datakeys_from_dict(keys)
+        data_keys, invalid_keys = self._read_datakeys_from_dict(keys)
+        invalid_data = {i: data.pop(i) for i in invalid_keys} if invalid_keys else None
+        keys = tuple(k for k in keys if k not in invalid_keys) if invalid_keys else keys
         data_unpacked = tuple(data.values())
 
-        return keys, data_keys, data_unpacked
+        return keys, data_keys, data_unpacked, invalid_data
 
-    def _read_datakeys_from_dict(self, keys: Sequence[str]) -> List[DataKey]:
+    def _read_datakeys_from_dict(self, keys: Sequence[str]) -> Tuple[List[DataKey], Optional[List[str]]]:
         def retrieve_key(key: str) -> DataKey:
             """Try to retrieve the datakey value by matching `<datakey>*`"""
             # Alias cases, like INPUT, will not be get by the enum iterator.
@@ -492,7 +501,15 @@ def retrieve_key(key: str) -> DataKey:
                 f"Your input data dictionary keys should start with some of datakey values: {allowed_dk}. Got `{key}`"
             )
 
-        return [DataKey.get(retrieve_key(k)) for k in keys]
+        valid_data_keys = []
+        invalid_keys = []
+        for k in keys:
+            try:
+                valid_data_keys.append(DataKey.get(retrieve_key(k)))
+            except ValueError:
+                invalid_keys.append(k)
+
+        return valid_data_keys, invalid_keys
 
     def _preproc_mask(self, arg: MaskDataType) -> MaskDataType:
         if isinstance(arg, list):
diff --git a/tests/augmentation/test_container.py b/tests/augmentation/test_container.py
index 3d58a621f3..f0dcff64b1 100644
--- a/tests/augmentation/test_container.py
+++ b/tests/augmentation/test_container.py
@@ -776,13 +776,14 @@ def test_dict_as_input_forward_and_inverse(self, random_apply, bbox_key, device,
             random_apply=random_apply,
         )
 
-        data = {"input": inp, "mask": mask, bbox_key: bbox, "keypoints": keypoints}
+        data = {"input": inp, "mask": mask, bbox_key: bbox, "keypoints": keypoints, "id": 45}
         out = aug(data)
         assert out["input"].shape == inp.shape
         assert out["mask"].shape == mask.shape
         assert out[bbox_key].shape == bbox.shape
         assert out["keypoints"].shape == keypoints.shape
         assert set(out["mask"].unique().tolist()).issubset(set(mask.unique().tolist()))
+        assert out["id"] == 45
 
         out_inv = aug.inverse(out)
         assert out_inv["input"].shape == inp.shape
@@ -790,6 +791,7 @@ def test_dict_as_input_forward_and_inverse(self, random_apply, bbox_key, device,
         assert out_inv[bbox_key].shape == bbox.shape
         assert out_inv["keypoints"].shape == keypoints.shape
         assert set(out_inv["mask"].unique().tolist()).issubset(set(mask.unique().tolist()))
+        assert out_inv["id"] == 45
 
         if random_apply is False:
             reproducibility_test(data, aug)