Row 2200

Row ID: 2200 | Dataset Entry | Axioma AXP Content Repository

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I'm trying to learn VAE and I'm pretty clear about the idea of (vanilla) AE and its internal workings. I understand that VAE is an extension of AE for most part where the fixed latent vector in the middle is not replace with mean vector and stdev vector and we do sampling from them (Yes, using reparametrization technique to not mess with gradient flow). But I still can't wrap my head around mean vector and stdev vector, it is mean and stdev along which axis(or dimension)? Why are we trying to do this sampling? Also can you explain its loss function in simple terms (you may assume that I know KL div)

FieldValue
text I'm trying to learn VAE and I'm pretty clear about the idea of (vanilla) AE and its internal workings. I understand that VAE is an extension of AE for most part where the fixed latent vector in the middle is not replace with mean vector and stdev vector and we do sampling from them (Yes, using reparametrization technique to not mess with gradient flow). But I still can't wrap my head around mean vector and stdev vector, it is mean and stdev along which axis(or dimension)? Why are we trying to do…
label r/tensorflow
dataType post
communityName r/tensorflow
datetime 2023-07-14
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{
  "text": "I'm trying to learn VAE and I'm pretty clear about the idea of (vanilla) AE and its internal workings. I understand that VAE is an extension of AE for most part where the fixed latent vector in the middle is not replace with mean vector and stdev vector and we do sampling from them (Yes, using reparametrization technique to not mess with gradient flow). But I still can't wrap my head around mean vector and stdev vector, it is mean and stdev along which axis(or dimension)? Why are we trying to do this sampling? Also can you explain its loss function in simple terms (you may assume that I know KL div)",
  "label": "r/tensorflow",
  "dataType": "post",
  "communityName": "r/tensorflow",
  "datetime": "2023-07-14",
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}
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