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https://arxiv.org/pdf/1702.05464.pdf
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They also can i…
leo-p updated
7 years ago
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Exampleにドメイン適応用の事例を作りたい。現状はドメイン汎化のみ。
具体的には、
pytransfer.trainer.pyの中に、ドメイン適応用のLearner(DALearner)を作るのと、データセットを読み込む部分を作れば良い、はず。
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Summary of request: Add a new organization to ROR
Name of organization: North Central Climate Adaptation Science Center*en
Website: https://nccasc.colorado.edu
Domains:
Link to publications:
…
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Hi, I'm very interested in your work, and what datastets do you use for this task?
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### Question
Is it possible to do Domain Adaptation instead of Task Adaptation?
Specifically, I want to use LLaVA as a starting checkpoint to train the language and images of the new domain.
thank …
unmo updated
7 months ago
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Impressive work. Can' t wait to get the code. so when?!
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Hello,
The front-end module is used in the paper "FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation". Do you know when the .prototxt files used in the paper will be made av…
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https://arxiv.org/pdf/1607.03516.pdf
In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition. Specifically, we design a new m…
leo-p updated
7 years ago
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In your paper, you add a Geodesic Correlation Alignment and Progressive Domain Calibration to deal with domain adaptation. But I can not find it in your code. Could you provide that part of code?
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Hi. GDmicro is really impressive, thanks for your remarkable job.
I want to check the performance with the final test data I have.
Is it correct to set the class column value of my final test data…