Row 6455

Row ID: 6455 | Dataset Entry | Axioma AXP Content Repository

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I'm trying to understand how ann vs cnn works.

Essentially network is just leaning a mapping function from input to output. But in context of ANN where feature space is represented by data as a dot in N dims feature space. The boundries are non linear and drawn which sperates the feature space.

But w.r.t CNN, what is high dimensional space and feature space? Is this every pixel value in 3d space is this where boundries are drawn like ANN ? But I realise that decesion boundries are drawn on learnt features by cnn . Meaning, in the last layers where filters are more context specific that's where the boundries are drawn.

I want to know 1. Is my understanding correct, I'm confused 2. Do these feature space move or change or transform as the n/w learns, forming a cluster with seperable spaces or does lines curve and cluster without moving feature space ? 3. In ANN boundaries are on raw high dimensional points whereas in cnn boundries are on learn kernel features why???

I can't wrap my head around how it works at a fundamental level .. plz help I'm stuck ..

FieldValue
text I'm trying to understand how ann vs cnn works. Essentially network is just leaning a mapping function from input to output. But in context of ANN where feature space is represented by data as a dot in N dims feature space. The boundries are non linear and drawn which sperates the feature space. But w.r.t CNN, what is high dimensional space and feature space? Is this every pixel value in 3d space is this where boundries are drawn like ANN ? But I realise that decesion boundries are drawn on l…
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-05-10
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{
  "text": "I'm trying to understand how ann vs cnn works. \n\nEssentially network is just leaning a mapping function from input to output. But in context of ANN where feature space is represented by data as a dot in N dims feature space. The boundries are non linear and drawn which sperates the feature space. \n\nBut w.r.t CNN, what is high dimensional space and feature space? Is this every pixel value in 3d space is this where boundries are drawn like ANN ? But I realise that decesion boundries are drawn on learnt features by cnn . Meaning, in the last layers where filters are more context specific that's where the boundries are drawn. \n\n\nI want to know \n1. Is my understanding correct, I'm confused\n2. Do these feature space move or change or transform as the n/w learns, forming a cluster with seperable spaces or does lines curve and cluster without moving feature space ?\n3. In ANN boundaries are on raw high dimensional points whereas in cnn boundries are on learn kernel features why??? \n\nI can't wrap my head around how it works at a fundamental level .. plz help I'm stuck .. ",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-10",
  "username_encoded": "Z0FBQUFBQm5LakwySm5ELUR6aG9mUjlUVG5pMVFzdlh0QVNGM2ZnaGFEU1ZSakNPemxqblhqbDFCYzVLWFVXdU40dGkxZGRnNC1ESWR1d2NTeHlKWFRWRkJGNjBPTXd2VHc9PQ==",
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}
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