diff --git a/test/test_bias_jac_t_mat_prod.py b/test/test_bias_jac_t_mat_prod.py
index 2d8ba112..45c79f23 100644
--- a/test/test_bias_jac_t_mat_prod.py
+++ b/test/test_bias_jac_t_mat_prod.py
@@ -5,7 +5,7 @@
 """
 import pytest
 import torch
-from torch.nn import Conv2d, Linear
+from torch.nn import Conv2d, ConvTranspose2d, Linear
 
 from backpack import extend
 from backpack.hessianfree.lop import transposed_jacobian_vector_product
@@ -33,6 +33,14 @@ def make_id(layer, input_shape, sum_batch):
         (3, 2, 11, 13),
         True,
     ],
+    [ConvTranspose2d(2, 3, kernel_size=2), (3, 2, 11, 13), True],
+    [ConvTranspose2d(2, 3, kernel_size=2, padding=1), (3, 2, 11, 13), True],
+    [ConvTranspose2d(2, 3, kernel_size=2, padding=1, stride=2), (3, 2, 11, 13), True],
+    [
+        ConvTranspose2d(2, 3, kernel_size=2, padding=1, stride=2, dilation=2),
+        (3, 2, 11, 13),
+        True,
+    ],
     # sum_batch = False
     [Linear(5, 1), (3, 5), False],
     [Linear(20, 10), (5, 20), False],
@@ -44,6 +52,14 @@ def make_id(layer, input_shape, sum_batch):
         (3, 2, 11, 13),
         False,
     ],
+    [ConvTranspose2d(2, 3, kernel_size=2), (3, 2, 11, 13), False],
+    [ConvTranspose2d(2, 3, kernel_size=2, padding=1), (3, 2, 11, 13), False],
+    [ConvTranspose2d(2, 3, kernel_size=2, padding=1, stride=2), (3, 2, 11, 13), False],
+    [
+        ConvTranspose2d(2, 3, kernel_size=2, padding=1, stride=2, dilation=2),
+        (3, 2, 11, 13),
+        False,
+    ],
 ]
 IDS = [
     make_id(layer, input_shape, sum_batch)
diff --git a/test/test_weight_jac_t_mat_prod.py b/test/test_weight_jac_t_mat_prod.py
index a263233f..801bb700 100644
--- a/test/test_weight_jac_t_mat_prod.py
+++ b/test/test_weight_jac_t_mat_prod.py
@@ -14,6 +14,7 @@
 from .test_bias_jac_t_mat_prod import ARGS, SETTINGS, make_id
 from .test_ea_jac_t_mat_jac_prod import derivative_from_layer, get_output_shape
 
+SETTINGS = [s for s in SETTINGS if not isinstance(s[0], torch.nn.ConvTranspose2d)]
 IDS = [
     make_id(layer, input_shape, sum_batch)
     for (layer, input_shape, sum_batch) in SETTINGS