diff --git a/onnxscript/function_libs/torch_lib/ops/nn.py b/onnxscript/function_libs/torch_lib/ops/nn.py
index de89ff6bad..c2cf8fd49d 100644
--- a/onnxscript/function_libs/torch_lib/ops/nn.py
+++ b/onnxscript/function_libs/torch_lib/ops/nn.py
@@ -2330,6 +2330,7 @@ def _aten_upsample_output_size(
     coordinate_transformation_mode: str,
     antialias: int = 0,
     cubic_coeff_a: float = -0.75,
+    exclude_outside: int = 0,
 ) -> TReal:
     batch_and_channel = op.Shape(self, end=2, start=0)
     # When output_size is passed in as a list of integers, the torch.onnx
@@ -2348,6 +2349,7 @@ def _aten_upsample_output_size(
         cubic_coeff_a=cubic_coeff_a,
         nearest_mode="floor",
         antialias=antialias,
+        exclude_outside=exclude_outside,
     )
 
 
@@ -2358,6 +2360,7 @@ def _aten_upsample_scales(
     coordinate_transformation_mode: str,
     antialias: int = 0,
     cubic_coeff_a: float = -0.75,
+    exclude_outside: int = 0,
 ) -> TReal:
     return op.Resize(
         self,
@@ -2371,6 +2374,7 @@ def _aten_upsample_scales(
         cubic_coeff_a=cubic_coeff_a,
         nearest_mode="floor",
         antialias=antialias,
+        exclude_outside=exclude_outside,
     )
 
 
@@ -2410,6 +2414,10 @@ def aten__upsample_bicubic2d_aa(
     coordinate_transformation_mode = _get_upsample_align_corners_mode(align_corners)
     # PyTorch uses cubic_coeff_a=-0.5 (Keys interpolation, PIL-compatible) when
     # antialias=True, as opposed to -0.75 (OpenCV-compatible) for the non-antialias case.
+    # exclude_outside=1 matches PyTorch's antialias kernel, which drops out-of-bounds
+    # samples from the filter window and renormalizes the remaining weights so they
+    # sum to 1. Without it the ONNX Resize keeps phantom out-of-bounds weight in the
+    # denominator and produces values that differ from eager near the boundary.
     return _aten_upsample_output_size(
         self,
         output_size,
@@ -2417,6 +2425,7 @@ def aten__upsample_bicubic2d_aa(
         coordinate_transformation_mode=coordinate_transformation_mode,
         antialias=1,
         cubic_coeff_a=-0.5,
+        exclude_outside=1,
     )
 
 
@@ -2495,12 +2504,17 @@ def aten__upsample_bilinear2d_aa(
     # NOTE: Based on experimentation, scales_h and scales_w are always ignored in PyTorch,
     # unless when align_corners is True, in which case we do not know what is going on.
     coordinate_transformation_mode = _get_upsample_align_corners_mode(align_corners)
+    # exclude_outside=1 matches PyTorch's antialias kernel, which drops out-of-bounds
+    # samples from the filter window and renormalizes the remaining weights so they
+    # sum to 1. Without it the ONNX Resize keeps phantom out-of-bounds weight in the
+    # denominator and produces values that differ from eager near the boundary.
     return _aten_upsample_output_size(
         self,
         output_size,
         coordinate_transformation_mode=coordinate_transformation_mode,
         mode="linear",
         antialias=1,
+        exclude_outside=1,
     )
 
 
diff --git a/tests/function_libs/torch_lib/ops_test_data.py b/tests/function_libs/torch_lib/ops_test_data.py
index 4bf98f587d..f4af54812e 100644
--- a/tests/function_libs/torch_lib/ops_test_data.py
+++ b/tests/function_libs/torch_lib/ops_test_data.py
@@ -1784,13 +1784,6 @@ def _where_input_wrangler(
     TorchLibOpInfo(
         "ops.aten._upsample_bilinear2d_aa",
         nn_ops.aten__upsample_bilinear2d_aa,
-        # ONNX and PyTorch use different anti-aliasing algorithms, so numerical results differ.
-        # However, the implementation is verified correct because:
-        # 1. The function correctly passes antialias=1 to ONNX Resize operation
-        # 2. Shape validation ensures the operation works correctly
-        # 3. Additional validation in test_aa_upsample_validation.py confirms correctness
-        # Shape-only comparison is the appropriate testing approach for this case.
-        compare_shape_only_for_output=(0,),
     ),
     TorchLibOpInfo("ops.aten.upsample_bilinear2d.vec", nn_ops.aten_upsample_bilinear2d_vec),
     TorchLibOpInfo(
@@ -1805,13 +1798,6 @@ def _where_input_wrangler(
     TorchLibOpInfo(
         "ops.aten._upsample_bicubic2d_aa",
         nn_ops.aten__upsample_bicubic2d_aa,
-        # ONNX and PyTorch use different anti-aliasing algorithms, so numerical results differ.
-        # However, the implementation is verified correct because:
-        # 1. The function correctly passes antialias=1 to ONNX Resize operation
-        # 2. Shape validation ensures the operation works correctly
-        # 3. Additional validation in test_aa_upsample_validation.py confirms correctness
-        # Shape-only comparison is the appropriate testing approach for this case.
-        compare_shape_only_for_output=(0,),
     ),
     TorchLibOpInfo("ops.aten.upsample_bicubic2d.vec", nn_ops.aten_upsample_bicubic2d_vec),
     TorchLibOpInfo(