diff --git a/torchvision/transforms/functional.py b/torchvision/transforms/functional.py
index 4b38c7bb92e..801df42a187 100644
--- a/torchvision/transforms/functional.py
+++ b/torchvision/transforms/functional.py
@@ -311,7 +311,7 @@ def normalize(tensor, mean, std, inplace=False):
     return tensor
 
 
-def resize(img: Tensor, size: List[int], interpolation: int = 2) -> Tensor:
+def resize(img: Tensor, size: List[int], interpolation: int = Image.BILINEAR) -> Tensor:
     r"""Resize the input image to the given size.
     The image can be a PIL Image or a torch Tensor, in which case it is expected
     to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
@@ -325,7 +325,9 @@ def resize(img: Tensor, size: List[int], interpolation: int = 2) -> Tensor:
             :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
             In torchscript mode padding as single int is not supported, use a tuple or
             list of length 1: ``[size, ]``.
-        interpolation (int, optional): Desired interpolation. Default is bilinear.
+        interpolation (int, optional): Desired interpolation enum defined by `filters`_.
+            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
+            and ``PIL.Image.BICUBIC`` are supported.
 
     Returns:
         PIL Image or Tensor: Resized image.
@@ -455,7 +457,9 @@ def resized_crop(
         height (int): Height of the crop box.
         width (int): Width of the crop box.
         size (sequence or int): Desired output size. Same semantics as ``resize``.
-        interpolation (int, optional): Desired interpolation. Default is ``PIL.Image.BILINEAR``.
+        interpolation (int, optional): Desired interpolation enum defined by `filters`_.
+            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
+            and ``PIL.Image.BICUBIC`` are supported.
     Returns:
         PIL Image or Tensor: Cropped image.
     """
diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index a403c54261f..f7d421d2b83 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -222,7 +222,9 @@ class Resize(torch.nn.Module):
             (size * height / width, size).
             In torchscript mode padding as single int is not supported, use a tuple or
             list of length 1: ``[size, ]``.
-        interpolation (int, optional): Desired interpolation. Default is ``PIL.Image.BILINEAR``
+        interpolation (int, optional): Desired interpolation enum defined by `filters`_.
+            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
+            and ``PIL.Image.BICUBIC`` are supported.
     """
 
     def __init__(self, size, interpolation=Image.BILINEAR):
@@ -703,7 +705,9 @@ class RandomResizedCrop(torch.nn.Module):
             made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
         scale (tuple of float): range of size of the origin size cropped
         ratio (tuple of float): range of aspect ratio of the origin aspect ratio cropped.
-        interpolation (int): Desired interpolation. Default: ``PIL.Image.BILINEAR``
+        interpolation (int): Desired interpolation enum defined by `filters`_.
+            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
+            and ``PIL.Image.BICUBIC`` are supported.
     """
 
     def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.BILINEAR):