Domain Adaption with different tasks and domains
$begingroup$
I know there exist plenty of deep learning algorithms for domain adaption (ADDA, DIRT-T, etc..), as long as the task keeps the same, e.g. I want to transfer knowledge from SVHN dataset to MNIST dataset, and recognize digits ranging from 0 to 9.
But whats about the different case, when the task changes, e.g. I have a nice classifier for recognizing digits from 0 to 5 for the SVHN dataset, and I want to transfer knowledge from this classifier to another one with different task and domain like recognizing digits 6-9 for the MNIST dataset.
Is the only option classical transfer-learning, with freezing/fine-tunining weights?
deep-learning transfer-learning domain-adaptation
asked 2 days ago
Andreas LookAndreas Look
38119
$endgroup$
add a comment |
$begingroup$
I know there exist plenty of deep learning algorithms for domain adaption (ADDA, DIRT-T, etc..), as long as the task keeps the same, e.g. I want to transfer knowledge from SVHN dataset to MNIST dataset, and recognize digits ranging from 0 to 9.
But whats about the different case, when the task changes, e.g. I have a nice classifier for recognizing digits from 0 to 5 for the SVHN dataset, and I want to transfer knowledge from this classifier to another one with different task and domain like recognizing digits 6-9 for the MNIST dataset.
Is the only option classical transfer-learning, with freezing/fine-tunining weights?
deep-learning transfer-learning domain-adaptation
asked 2 days ago
Andreas LookAndreas Look
38119
$endgroup$
add a comment |
$begingroup$
I know there exist plenty of deep learning algorithms for domain adaption (ADDA, DIRT-T, etc..), as long as the task keeps the same, e.g. I want to transfer knowledge from SVHN dataset to MNIST dataset, and recognize digits ranging from 0 to 9.
But whats about the different case, when the task changes, e.g. I have a nice classifier for recognizing digits from 0 to 5 for the SVHN dataset, and I want to transfer knowledge from this classifier to another one with different task and domain like recognizing digits 6-9 for the MNIST dataset.
Is the only option classical transfer-learning, with freezing/fine-tunining weights?
deep-learning transfer-learning domain-adaptation
asked 2 days ago
Andreas LookAndreas Look
38119
$endgroup$
I know there exist plenty of deep learning algorithms for domain adaption (ADDA, DIRT-T, etc..), as long as the task keeps the same, e.g. I want to transfer knowledge from SVHN dataset to MNIST dataset, and recognize digits ranging from 0 to 9.
But whats about the different case, when the task changes, e.g. I have a nice classifier for recognizing digits from 0 to 5 for the SVHN dataset, and I want to transfer knowledge from this classifier to another one with different task and domain like recognizing digits 6-9 for the MNIST dataset.
Is the only option classical transfer-learning, with freezing/fine-tunining weights?
deep-learning transfer-learning domain-adaptation
deep-learning transfer-learning domain-adaptation
asked 2 days ago
Andreas LookAndreas Look
38119
asked 2 days ago
Andreas LookAndreas Look
38119
asked 2 days ago
Andreas LookAndreas Look
38119
asked 2 days ago
Andreas LookAndreas Look
38119
asked 2 days ago
Andreas LookAndreas Look
38119
38119
add a comment |
add a comment |
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