Row 71332

Row ID: 71332 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 71332 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Are you sure that your data transforms are the same as they use in the paper? I noticed some strange things with the way you are handling them.

1. Cifar10 images are already 32x32 so there is no need to resize. Additionally that random crop of size 32 on an image of size 32x32 will mean that many of your train images will be very heavily masked. Which could explain the failure to generalize to your val set which has no such masking

2. As I said in another comment, the means and standard deviations for each channel post normalization are definitely not all .5 in your training dataset.

FieldValue
text Are you sure that your data transforms are the same as they use in the paper? I noticed some strange things with the way you are handling them. 1. Cifar10 images are already 32x32 so there is no need to resize. Additionally that random crop of size 32 on an image of size 32x32 will mean that many of your train images will be very heavily masked. Which could explain the failure to generalize to your val set which has no such masking 2. As I said in another comment, the means and standard deviat…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
username_encoded Z0FBQUFBQm5Lak1mYUJ0c2g0U1ROaVhKX3FxeEFCWmp2dUpWNlF2M01SMTcwN2FkNU5kbjgwUnI5Z1hDSHdmNEhpcF8xWVk0Y0h0MDE1VXBHaG5yTExWQkNKQ3MxWUtuS1E9PQ==
url_encoded Z0FBQUFBQm5Lak93bmtKd0NycHd6MzBjcXpNQTlFMkFRU0k3Zkg3bXBoWXJ2cjlWQ0JuZklNdVhjWUJqQUZaVWtIdFRwU2ZfNWFNSFB4Y09hbUVPUG5mQ2k4YmlqdUJJb0ItZXF6bTl3TVU5Y2VzTFgwVll0ODRfalY4ekhYMDVhaG85ejd4NVJvQXlTamt5LXE5RUtEcmJaLTZfVVJSR09mWTJnNzJ5ZVFuOHlRSlZ3Y2dhcEEyT2JKVll3TkZJc2RFMllibzQwU3VvS1Z0S18wUXNHcXRuYlpNQWN6ZEZ1bHhEOHlkeUFWNy1JT3BSQWtCNFBHYz0=

Raw Record

{
  "text": "Are you sure that your data transforms are the same as they use in the paper? I noticed some strange things with the way you are handling them.\n\n1. Cifar10 images are already 32x32 so there is no need to resize. Additionally that random crop of size 32 on an image of size 32x32 will mean that many of your train images will be very heavily masked. Which could explain the failure to generalize to your val set which has no such masking\n\n2. As I said in another comment, the means and standard deviations for each channel post normalization are definitely not all .5 in your training dataset.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1mYUJ0c2g0U1ROaVhKX3FxeEFCWmp2dUpWNlF2M01SMTcwN2FkNU5kbjgwUnI5Z1hDSHdmNEhpcF8xWVk0Y0h0MDE1VXBHaG5yTExWQkNKQ3MxWUtuS1E9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak93bmtKd0NycHd6MzBjcXpNQTlFMkFRU0k3Zkg3bXBoWXJ2cjlWQ0JuZklNdVhjWUJqQUZaVWtIdFRwU2ZfNWFNSFB4Y09hbUVPUG5mQ2k4YmlqdUJJb0ItZXF6bTl3TVU5Y2VzTFgwVll0ODRfalY4ekhYMDVhaG85ejd4NVJvQXlTamt5LXE5RUtEcmJaLTZfVVJSR09mWTJnNzJ5ZVFuOHlRSlZ3Y2dhcEEyT2JKVll3TkZJc2RFMllibzQwU3VvS1Z0S18wUXNHcXRuYlpNQWN6ZEZ1bHhEOHlkeUFWNy1JT3BSQWtCNFBHYz0="
}
Explore Dataset Explore Row

Entry Information