Row 6970

Row ID: 6970 | Dataset Entry | Axioma AXP Content Repository

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Hi everyone, i was experimenting with facial recognition, during my masters thesis and therefore was learning embeddings (using triplet loss, ArcFace, AdaCos... ect.). Intention was creating an efficient (and non gdpr violating) face unlock.

The ArcFace method appears to be the SOTA still. Works like AdaCos have tried eliminating the annoying hyperparameters by eliminating the margin and dynamicly adapting the scale during training, though in reality this doesn't seem to work as well as ArcFace when optimally tuned.

I subsequently came up with a different idea of adapting the margin during training instead of completely eliminating it, and in my tests it seemed to work very well, better than AdaCos and somewhere between equally well and slightly better than ArcFace. I would love to hear if someone could validate my findings, here is a pytorch implementation and explaination of the method: https://github.com/VBambi/AdaAcos-the-self-adjusting-implementaion-of-ArcFace

FieldValue
text Hi everyone, i was experimenting with facial recognition, during my masters thesis and therefore was learning embeddings (using triplet loss, ArcFace, AdaCos... ect.). Intention was creating an efficient (and non gdpr violating) face unlock. The ArcFace method appears to be the SOTA still. Works like AdaCos have tried eliminating the annoying hyperparameters by eliminating the margin and dynamicly adapting the scale during training, though in reality this doesn't seem to work as well as ArcFace…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-14
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{
  "text": "Hi everyone,\ni was experimenting with facial recognition, during my masters thesis and therefore was learning embeddings (using triplet loss, ArcFace, AdaCos... ect.). Intention was creating an efficient (and non gdpr violating) face unlock.\n\nThe ArcFace method appears to be the SOTA still. Works like AdaCos have tried eliminating the annoying hyperparameters by eliminating the margin and dynamicly adapting the scale during training, though in reality this doesn't seem to work as well as ArcFace when optimally tuned.\n\nI subsequently came up with a different idea of adapting the margin during training instead of completely eliminating it, and in my tests it seemed to work very well, better than AdaCos and somewhere between equally well and slightly better than ArcFace. I would love to hear if someone could validate my findings, here is a pytorch implementation and explaination of the method:\nhttps://github.com/VBambi/AdaAcos-the-self-adjusting-implementaion-of-ArcFace",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-14",
  "username_encoded": "Z0FBQUFBQm5Lakwzb0F5T2REeVdZT3p4LU10NEU1WEJsLTBSak5FUUMzSE1xUVNCU1Z4Nl92RFJvME00MzlvbmY5YjlkclVrNnF3eE10V2s4Z0JBT2E4MmNnbW9SOHMwY0E9PQ==",
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}
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