Questions tagged [domain-adaptation]
6 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
- 191
- 1
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3
votes
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
- 2
2
votes
1 answer
What does "aligned" across domains in domain adaptation?
within Delving into Local Features for Open-Set Domain Adaptation in Fundus Image Analysis paper. I got trouble in understanding their cluster-aware contrastive adaption $\mathcal{L}_\text{cca}$.
I don't get it how they defined "$i$ and $j$ are…
chickensoup
- 71
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1
vote
1 answer
Feature Transformation for Domain Adaptation: Modifying Abnormal Data to Match Normal Feature Distributions
Let $X$ be a dataset consisting of $N$ instances, where each instance is described by a set of features $\text{feat}_0, \ldots, \text{feat}_m$, and let $Y$ denote the corresponding target values. Suppose that $X$ is partitioned into two subsets:…
ABB
- 123
- 3
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
- 3
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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