Meaning of the Margin in a Contrastive Loss function
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I'm building a siamese network for a metric-learning task, using a contrastive loss function, and I'm uncertain on how to set the 'margin' hyperparameter for the loss, or how to interpret the value. I see what it means with respect to the loss function, but couldn't the neural network just learn to project outputs that suit any given margin? What heuristics can I use to pick a value or range?
My inputs to the loss function are currently 1024-dimension dense embeddings from an RNN layer - Does the dimensionality of that input affect how I pick a margin? Should I use a dense layer to project it to a lower-dimensional space first? Any pointers on how to pick a specific margin value (or any relevant research) would be really appreciated! In case it matters, I'm using PyTorch.
neural-network deep-learning classification pytorch
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Vishnu MenonVishnu Menon
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I'm building a siamese network for a metric-learning task, using a contrastive loss function, and I'm uncertain on how to set the 'margin' hyperparameter for the loss, or how to interpret the value. I see what it means with respect to the loss function, but couldn't the neural network just learn to project outputs that suit any given margin? What heuristics can I use to pick a value or range?
My inputs to the loss function are currently 1024-dimension dense embeddings from an RNN layer - Does the dimensionality of that input affect how I pick a margin? Should I use a dense layer to project it to a lower-dimensional space first? Any pointers on how to pick a specific margin value (or any relevant research) would be really appreciated! In case it matters, I'm using PyTorch.
neural-network deep-learning classification pytorch
asked yesterday
Vishnu MenonVishnu Menon
1
New contributor
Vishnu Menon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
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I'm building a siamese network for a metric-learning task, using a contrastive loss function, and I'm uncertain on how to set the 'margin' hyperparameter for the loss, or how to interpret the value. I see what it means with respect to the loss function, but couldn't the neural network just learn to project outputs that suit any given margin? What heuristics can I use to pick a value or range?
My inputs to the loss function are currently 1024-dimension dense embeddings from an RNN layer - Does the dimensionality of that input affect how I pick a margin? Should I use a dense layer to project it to a lower-dimensional space first? Any pointers on how to pick a specific margin value (or any relevant research) would be really appreciated! In case it matters, I'm using PyTorch.
neural-network deep-learning classification pytorch
asked yesterday
Vishnu MenonVishnu Menon
1
New contributor
Vishnu Menon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm building a siamese network for a metric-learning task, using a contrastive loss function, and I'm uncertain on how to set the 'margin' hyperparameter for the loss, or how to interpret the value. I see what it means with respect to the loss function, but couldn't the neural network just learn to project outputs that suit any given margin? What heuristics can I use to pick a value or range?
My inputs to the loss function are currently 1024-dimension dense embeddings from an RNN layer - Does the dimensionality of that input affect how I pick a margin? Should I use a dense layer to project it to a lower-dimensional space first? Any pointers on how to pick a specific margin value (or any relevant research) would be really appreciated! In case it matters, I'm using PyTorch.
neural-network deep-learning classification pytorch
neural-network deep-learning classification pytorch
asked yesterday
Vishnu MenonVishnu Menon
1
New contributor
Vishnu Menon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Vishnu MenonVishnu Menon
1
New contributor
Vishnu Menon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Vishnu MenonVishnu Menon
1
New contributor
Vishnu Menon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Vishnu MenonVishnu Menon
1
asked yesterday
Vishnu MenonVishnu Menon
1
1
New contributor
Vishnu Menon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Vishnu Menon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Vishnu Menon is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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