Row 50179

Row ID: 50179 | Dataset Entry | Axioma AXP Content Repository

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Yes, the noise and the associated labels you introduce would need to depend on careful consideration of the data distribution. Perhaps a more appropriate technique example would be convex combinations of images from different classes where the label vector is the class weights in the combination. This would at least give you some intution for how close the ambiguous data is to a real image.

Though I am reminded of the fact that the Fourier spectrum of gaussian noise is uniform, whereas the spectrum of true samples almost certainly follows some meaningful distribution.

A final thought would be to add a label for "I'm uncertain" to the model's outputs, and rather than forcing the model to choose a high entropy prediction from the true label dimensions, the ambiguous data would be assigned this label.

FieldValue
text Yes, the noise and the associated labels you introduce would need to depend on careful consideration of the data distribution. Perhaps a more appropriate technique example would be convex combinations of images from different classes where the label vector is the class weights in the combination. This would at least give you some intution for how close the ambiguous data is to a real image. Though I am reminded of the fact that the Fourier spectrum of gaussian noise is uniform, whereas the spe…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-22
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Raw Record

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  "text": "Yes, the noise and the associated labels you introduce would need to depend on careful consideration of the data distribution.  Perhaps a more appropriate technique example would be convex combinations of images from different classes where the label vector is the class weights in the combination. This would at least give you some intution for how close the ambiguous data is to a real image.\n\nThough I am reminded of the fact that the Fourier spectrum of gaussian noise is uniform, whereas the spectrum of true samples almost certainly follows some meaningful distribution.\n\nA final thought would be to add a label for \"I'm uncertain\" to the model's outputs, and rather than forcing the model to choose a high entropy prediction from the true label dimensions, the ambiguous data would be assigned this label.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
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