Row 50069

Row ID: 50069 | Dataset Entry | Axioma AXP Content Repository

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I feel like I'm missing a keyword here, so maybe you guys can help me figure out what to search for.

I was recently thinking about what occurs when you give a typical classifier a completely random sample that is effectively out of distribution. It will still return a label. For instance, if I train a model to classify MNIST, and I feed it vectors of Gaussian noise, the model will classify these completely random vectors as digits.

Has anyone attempted to augment their training datasets with random noise (or random combinations of training examples)? In this strategy, my intuition is that you'd be injecting essentially negative counter examples - and you'd want the label vectors to have a high amount of entropy. I think these counter examples would have a high degree of aleatoric uncertainty.

FieldValue
text I feel like I'm missing a keyword here, so maybe you guys can help me figure out what to search for. I was recently thinking about what occurs when you give a typical classifier a completely random sample that is effectively out of distribution. It will still return a label. For instance, if I train a model to classify MNIST, and I feed it vectors of Gaussian noise, the model will classify these completely random vectors as digits. Has anyone attempted to augment their training datasets with r…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-22
username_encoded Z0FBQUFBQm5Lak1TcTFwbDE1OTJrdlZCM1pXYV9iYnppbnVhUnMwdllhZzRnWFJJTXlNQk1IRnpxR0hlZFJacllZMjg4YndhYUw1N3FiZW9kOUU1b1dnQjFrRW1udFJXT3c9PQ==
url_encoded Z0FBQUFBQm5Lak9pcy1ybUVYdWVKTTRNNFBwUTRTOVM5MGVzN3pETTFTVk9kR1YxTWZwWUlDLXJYN0RpWGVlRGYtT25JZTdYS09fRnE2bDNrWndPcktUeURqcV80MjY3Q0ZJMmdsNEg5Q0NMODVvRVJWUGtua21lTndxeUFrR1Y2Ym9RQjBTakQ0VFJOSEVEb3ViS0U3VzZrOFpId3NFREUteUpEaDNLcHBqNF9mWDdwdFZpc1dsdFkwclFTYWNoVkdJQVhiWk1QV3l0czlGOGlucnVwdmZYd0c3WjA1Wm9pdz09

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{
  "text": "I feel like I'm missing a keyword here, so maybe you guys can help me figure out what to search for.\n\nI was recently thinking about what occurs when you give a typical classifier a completely random sample that is effectively out of distribution. It will still return a label. For instance, if I train a model to classify MNIST, and I feed it vectors of Gaussian noise, the model will classify these completely random vectors as digits.\n\nHas anyone attempted to augment their training datasets with random noise (or random combinations of training examples)? In this strategy, my intuition is that you'd be injecting essentially negative counter examples - and you'd want the label vectors to have a high amount of entropy. I think these counter examples would have a high degree of aleatoric uncertainty. ",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1TcTFwbDE1OTJrdlZCM1pXYV9iYnppbnVhUnMwdllhZzRnWFJJTXlNQk1IRnpxR0hlZFJacllZMjg4YndhYUw1N3FiZW9kOUU1b1dnQjFrRW1udFJXT3c9PQ==",
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}
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