Row 5360

Row ID: 5360 | Dataset Entry | Axioma AXP Content Repository

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Hello, I just learned deep learning for my college needs.

After I browse articles on the internet, many say that data augmentation should be applied only to training data after data splitting (training:validation:testing).

But in my case, I have imbalanced data. If I only apply augmentation to the training data, the validation and testing data will still be unbalanced. (Is this a problem?)

What is the best practice for this? I hope you can help me.

Thank you!

Note: This is image classification project

FieldValue
text Hello, I just learned deep learning for my college needs. After I browse articles on the internet, many say that data augmentation should be applied only to training data after data splitting (training:validation:testing). But in my case, I have imbalanced data. If I only apply augmentation to the training data, the validation and testing data will still be unbalanced. (Is this a problem?) What is the best practice for this? I hope you can help me. Thank you! Note: This is image classifica…
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-04-29
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{
  "text": "Hello, I just learned deep learning for my college needs.\n\nAfter I browse articles on the internet, many say that data augmentation should be applied only to training data after data splitting (training:validation:testing). \n\nBut in my case, I have imbalanced data. If I only apply augmentation to the training data, the validation and testing data will still be unbalanced.\n(Is this a problem?)\n\nWhat is the best practice for this? I hope you can help me.\n\nThank you!\n\nNote: This is image classification project\n\n",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-04-29",
  "username_encoded": "Z0FBQUFBQm5LakwySktWR0xSZEpyd2ViT3NfOTNLdnVZNXcyME4tT3B3TE1QTElucW9hRWdCUWZMRGhYSnIwelpnd3VuQ1h2QjV4MjZ4QU5mMUtacWdTcmQ2OEJGYWp6RnQ1XzUzdHNBR19JaXRfazl5ZDVLRXc9",
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}
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