Hello,
I am using the ResNet architecture as a feature extractor for an OCR task. While debugging I noticed that the output values of ResNet are very similar for any input image out of my dataset. So I decided to look at the output values when an untrained ResNet is given random input.
resnet = torchvision.models.resnet18(pretrained=False)

resnet.eval()
for _ in range(10):
    output = resnet(torch.rand([1, 3, 224, 224]))
    print(output[0][450:550]) # holds for any range

Which yields:
tensor([-1.1587, -1.1450,  1.5259,  0.6780, -1.1898,  1.3276,  1.4065,  0.8970,
        -1.2454, -1.0678, -1.4614,  0.9561,  1.1011, -0.6139,  0.8475, -0.9382,
         0.7540, -0.1661,  0.4512, -1.1243,  0.4361,  0.3670,  1.2436,  0.3186,
         1.5027, -0.4249, -0.5414,  2.1813,  0.8397, -0.9550, -0.6832,  1.1566,
        -2.1656, -1.3857,  0.3118, -0.2025,  2.1524,  0.4039,  0.0289, -1.1378,
         0.9507,  2.2936,  0.1370, -0.9087, -0.5366, -0.1129,  0.6830, -0.2568,
         0.6538,  0.6729, -0.5142,  0.0746, -0.5973, -0.0693,  0.3187,  0.6297,
         0.8349,  0.9990,  0.2052,  1.3444,  0.9707,  0.6360,  1.7593, -1.1037,
         2.7053, -0.3235, -1.3606,  0.1587, -2.3617,  0.9869, -0.4764, -0.7188,
        -0.3749,  0.4475, -1.5170,  1.0435,  0.8179,  1.0773,  0.1973,  0.8055,
         2.1609,  0.6247, -1.4064, -0.4369,  0.3356, -0.3441,  0.6190, -0.4036,
        -0.7672, -0.4250, -1.9830, -0.7269, -0.4921,  1.2250,  0.1022,  0.3778,
        -0.4552, -1.3471, -1.8389,  0.6905], grad_fn=<SliceBackward>)
tensor([-1.1920, -1.1297,  1.5316,  0.7081, -1.1729,  1.3323,  1.4214,  0.9075,
        -1.2718, -1.0987, -1.4925,  0.9335,  1.0910, -0.5857,  0.8446, -0.9113,
         0.7385, -0.1656,  0.4398, -1.1169,  0.4131,  0.3641,  1.2779,  0.3293,
         1.5286, -0.4296, -0.5640,  2.1615,  0.8044, -0.9519, -0.6629,  1.1419,
        -2.1183, -1.4250,  0.3177, -0.2175,  2.1561,  0.3962,  0.0535, -1.1837,
         0.9439,  2.2942,  0.0923, -0.8926, -0.5461, -0.1261,  0.6669, -0.2641,
         0.6605,  0.6535, -0.4878,  0.0700, -0.5891, -0.0443,  0.3056,  0.6454,
         0.8514,  1.0222,  0.1479,  1.3382,  0.9450,  0.5977,  1.7593, -1.1386,
         2.6689, -0.3277, -1.3361,  0.1698, -2.3721,  0.9978, -0.4780, -0.7501,
        -0.3701,  0.4395, -1.5692,  1.0734,  0.8103,  1.0783,  0.2129,  0.8095,
         2.1406,  0.5958, -1.3652, -0.4346,  0.3335, -0.3429,  0.6060, -0.4097,
        -0.7660, -0.4304, -1.9753, -0.7312, -0.5193,  1.2653,  0.1605,  0.4289,
        -0.4405, -1.3589, -1.8204,  0.7157], grad_fn=<SliceBackward>)

for the first two random inputs. As one can see, the output values are very similar. This similarity holds for other random inputs and all output values as well from what I can tell.
This is surprising to me because even if the untrained ResNet architecture is initialised with random weights, I do not expect to have similar outputs values with different inputs.
I would greatly appreciate an answer as to why these output values for completely different inputs would be so similar on an untrained network.