Row 16365

Row ID: 16365 | Dataset Entry | Axioma AXP Content Repository

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This is an interesting question that shouldn't be too tough to test out! Create a dataset from your multimodal vectors with the labels being the modality, and then train a MLP to predict modality from vectors.

I would guess it depends on your specific model/task (and like the other poster mentioned, whether you are feeding extra information in to represent modality, like sentinel tokens) but I would guess that in most cases there should be artifacts in the vector that come from each encoder in a way that a model could use to tell them apart.

FieldValue
text This is an interesting question that shouldn't be too tough to test out! Create a dataset from your multimodal vectors with the labels being the modality, and then train a MLP to predict modality from vectors. I would guess it depends on your specific model/task (and like the other poster mentioned, whether you are feeding extra information in to represent modality, like sentinel tokens) but I would guess that in most cases there should be artifacts in the vector that come from each encoder in…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
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{
  "text": "This is an interesting question that shouldn't be too tough to test out! Create a dataset from your multimodal vectors with the labels being the modality, and then train a MLP to predict modality from vectors. \n\nI would guess it depends on your specific model/task (and like the other poster mentioned, whether you are feeding extra information in to represent modality, like sentinel tokens) but I would guess that in most cases there should be artifacts in the vector that come from each encoder in a way that a model could use to tell them apart.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-20",
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