diff --git a/test/dattri/algorithm/test_influence_function.py b/test/dattri/algorithm/test_influence_function.py
index 2b86b7b..7cc3945 100644
--- a/test/dattri/algorithm/test_influence_function.py
+++ b/test/dattri/algorithm/test_influence_function.py
@@ -312,3 +312,58 @@ def f(params, data_target_pair):
         assert (cached_train_reps.shape[0] == 4 * math.ceil(0.5 * len(train_loader)))
         # Check for transformed test rep
         assert (torch.allclose(ground_truth_test_rep, transformed_test_rep, atol=1e-4))
+
+    def test_ekfac_transform_test_rep(self):
+        """Test for EK-FAC test representation transformation."""
+        def average_pairwise_correlation(tensor1, tensor2):
+            stacked = torch.stack([tensor1, tensor2], dim=0)
+            reshaped = stacked.view(2, -1)
+
+            corr_matrix = torch.corrcoef(reshaped)
+            pairwise_corr = corr_matrix[0, 1]
+
+            return pairwise_corr.item()
+
+        train_dataset = TensorDataset(
+            torch.randn(20, 1, 28, 28),
+            torch.randint(0, 10, (20,)),
+        )
+        train_loader = DataLoader(train_dataset, batch_size=4)
+
+        model = train_mnist_lr(train_loader)
+
+        def f(params, data_target_pair):
+            image, label = data_target_pair
+            loss = nn.CrossEntropyLoss()
+            yhat = torch.func.functional_call(model, params, image)
+            return loss(yhat, label.long())
+
+        task = AttributionTask(
+            loss_func=f,
+            model=model,
+            checkpoints=model.state_dict(),
+        )
+
+        # EK-FAC
+        attributor = IFAttributorEKFAC(
+            task=task,
+            device=torch.device("cpu"),
+            damping=0.1,
+        )
+        attributor.cache(train_loader)
+
+        attributor_gt = IFAttributorExplicit(
+            task=task,
+            layer_name=attributor.layer_name,
+            device=torch.device("cpu"),
+            regularization=1e-3,
+        )
+        attributor_gt.cache(train_loader)
+
+        test_rep = torch.randn((30, 7850), device=torch.device("cpu"))
+        transformed_test_rep = attributor.transform_test_rep(0, test_rep)
+        gt_test_rep = attributor_gt.transform_test_rep(0, test_rep[:, :7840])
+
+        # Check pair-wise correlation
+        corr = average_pairwise_correlation(gt_test_rep, transformed_test_rep)
+        assert corr > 0.98  # noqa: PLR2004