Row 17344

Row ID: 17344 | Dataset Entry | Axioma AXP Content Repository

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I believe you’re entangling the concept of input size and upsampling with a particular interpolant in terms of its effect on pixels in the input image as a pre processing operation.

If your model is trained on images that have been upsampled with a bicubic interpolant, that affects the smoothness of the pixels in the output upsampled image as they are averaged weighed by the interpolant. The filters in your convnet have been trained to expect similarly preprocessed content, regardless of input grid size.

As an experiment, take your upsampled images and then crop a portion of them and perform inference with your trained CNN.

Does that work well? I expect yes. We’re not talking about input size, we’re talking about preserving the features in the input images at inference time to be the same as in training.

FieldValue
text I believe you’re entangling the concept of input size and upsampling with a particular interpolant in terms of its effect on pixels in the input image as a pre processing operation. If your model is trained on images that have been upsampled with a bicubic interpolant, that affects the smoothness of the pixels in the output upsampled image as they are averaged weighed by the interpolant. The filters in your convnet have been trained to expect similarly preprocessed content, regardless of input …
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-20
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Raw Record

{
  "text": "I believe you’re entangling the concept of input size and upsampling with a particular interpolant in terms of its effect on pixels in the input image as a pre processing operation.\n\nIf your model is trained on images that have been upsampled with a bicubic interpolant, that affects the smoothness of the pixels in the output upsampled image as they are averaged weighed by the interpolant. The filters in your convnet have been trained to expect similarly preprocessed content, regardless of input grid size.\n\nAs an experiment, take your upsampled images and then crop a portion of them and perform inference with your trained CNN. \n\nDoes that work well? I expect yes. We’re not talking about input size, we’re talking about preserving the features in the input images at inference time to be the same as in training.",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-20",
  "username_encoded": "Z0FBQUFBQm5Lakw5ZmV3X1RaTnp4cTdPbmlSV0tSR2U3TkpZb21vczk2dGRFMmVmcDV2NVJ4Zkk1bllkWVA1RVdTVkozdHhPcm5tTFFmQTdYUkh0U1Fqd0hfNGo2ZV9yRGc9PQ==",
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}
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