Questions tagged [domain-adaptation]
7 questions
6
votes
2 answers
What is the difference between learning without forgetting and transfer learning?
I would like to incrementally train my model with my current dataset and I asked this question on Github, which is what I'm using SSD MobileNet v1.
Someone there told me about learning without forgetting. I'm now confused between learning without…
abhimanyuaryan
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3
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1 answer
What's the difference between domain randomization and domain adaptation?
In my understanding, domain randomization is one method of diversifying the dataset to achieve a better shot at domain adaptation. Am I wrong?
Taro Yehai
- 131
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1
vote
1 answer
Training a classifier on different datasets with different image conditions for different labels causes the model to infer using the background
I have an interesting problem related to training the model on two different datasets for the target feature on images taken on different conditions, which might affect the model's ability to generalize.
To explain I will give examples of images…
Mohammed Alkhrashi
- 123
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1
vote
0 answers
Why is domain adaptation and generative modelling for knowledge graphs still not applied widely in enterprise data? What are the challenges?
I see that domain adaptation and transfer learning has been widely adopted in image classification and semantic segmentation analysis. But it's still lacking in providing solutions to enterprise data, for example, solving problems related to…
Jey
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Which meta-learning algorithms are well-suited for "many-shot learning" scenarios, where the target training set is large?
Much of the meta-learning literature deals with the few-shot learning problem of using data from a diverse set of "source" tasks (the meta-dataset) in order to train a model that can quickly learn how to solve a new, previously unseen "target" task.…
Ori
- 101
0
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0 answers
Can we naively merge source and target datasets to train for the same task instead of performing domain adaptation?
I have seen from literature that models such as DANN or ADDA are typical in the field of Domain Adaptation, a branch of transductive learning. I know that these methods are extremely useful especially when we try to perform the same tasks as for…
Haneul
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0
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0 answers
How to perform domain adaptation if there are only unlabelled data in both source and target domains
Recently I am reading literature regarding domain adaption. However, most of the works consider scenarios when there are some labelled data in the source domain. So I wonder if there is any unsupervised way of domain adaption when there are only…
