Row 44607

Row ID: 44607 | Dataset Entry | Axioma AXP Content Repository

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Can someone explain to me how VAEs actually get trained? I am really stuck on this.

I understand the theoretical benefit of normalizing the latent space. But every explanation makes it seem like during training we draw from a random distribution. Wouldn't this just result in muddy model outputs that don't converge because we have random inputs.

Say we have 2x = y and are making a model. A normal AE would obviously see the correlation between y and x:

0 -> 0 1 -> 2 2 -> 4

But if we drop a random sampling in there during training, the data could be any random set from the distribution:

x = 0 -> random sample = 1 -> y = 0

x = 1 -> random sample = 0 -> y = 2

x = 2 -> random sample = 0 -> y = 4

And this would obviously not get a good answer if we trained on it.

The only thing I can think of is if VAEs are trained on the z-score instead of a random sample, it would maintain the normalization and the relative value of the inputs.

FieldValue
text Can someone explain to me how VAEs actually get trained? I am really stuck on this. I understand the theoretical benefit of normalizing the latent space. But every explanation makes it seem like during training we draw from a random distribution. Wouldn't this just result in muddy model outputs that don't converge because we have random inputs. Say we have 2x = y and are making a model. A normal AE would obviously see the correlation between y and x: 0 -> 0 1 -> 2 2 -> 4 But if we d…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-22
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Raw Record

{
  "text": "Can someone explain to me how VAEs actually get trained? I am really stuck on this.\n\n\n\nI understand the theoretical benefit of normalizing the latent space. But every explanation makes it seem like during training we draw from a random distribution. Wouldn't this just result in muddy model outputs that don't converge because we have random inputs.\n\n  \nSay we have 2x = y and are making a model. A normal AE would obviously see the correlation between y and x:\n\n0 -> 0  \n1 -> 2  \n2 -> 4\n\nBut if we drop a random sampling in there during training, the data could be any random set from the distribution:\n\nx = 0 -> random sample = 1 -> y = 0\n\nx = 1 -> random sample = 0 -> y = 2\n\nx = 2 -> random sample = 0 -> y = 4\n\nAnd this would obviously not get a good answer if we trained on it.\n\n  \nThe only thing I can think of is if VAEs are trained on the z-score instead of a random sample, it would maintain the normalization and the relative value of the inputs.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
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}
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