Questions tagged [domain-adaptation]

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What is the difference between Multi task learning and domain generalization

I was wondering about the differences between "multi-task learning" and "domain generalization". It seems to me that both of them are types of inductive transfer learning but I'm ...
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6 views

Train consistent embeddings using text from different domains

I would like to train text embeddings using texts from two different domains (podcast summaries and movie summaries). The embeddings should capture similarities on topics the texts talk about, but ...
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0answers
33 views

Computing symmetric difference hypothesis divergence $H \Delta H$ for two domains using a segmentation network

Given two domains $D_1$ and $D_2$, the symmetric difference hypothesis divergence ($H \Delta H$) is used as a measure how much two domains differ from each other. Let the hypothesis, segmentation ...
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0answers
9 views

Training on a single, random domain, per batch vs multiple domains per batch on a common task

Say I have multiple domains such that d_i is drawn from D=[d_1, d_2, ... d_K]. We have two options to train a CNN which equally ...
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0answers
24 views

How can I use transfer learning to predict height given age in Japan, using a model developed with USA data?

Suppose I have a (training) set of $n$ observation $\{(Y_i^{(U)},X_i^{(U)})\}_{i=1}^n$ of age $X_i^{(U)}$ and height $Y_i^{(U)}$ from people in the USA. Now suppose I also have a (test) set of $m$ ...
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0answers
11 views

Latent space for cross domain numerical features

I would like to find the shared latent space between two set of features. I have source and target domain features already extracted from images. I have 4 set of feature vectors for normal and ...
1
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0answers
23 views

Close set and open set classification at the same time

Is it possible to use a neural network(or another approach) to classify image based on trained data and at the same time if new image classes are introduced in the test set it should classify those ...
1
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1answer
28 views

Train on multi-domains, then fine-tune on specific domain

Would it make sense to first train a model on images from multiple domains, and then do "fine-tuning" on one specific domain to improve its performance on it? For instance, one could train an object ...
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1answer
220 views

How to measure the performance of a domain adaptation /Transfer learning technique? [closed]

Given that the performance you achieve depends on how far the target from the source domain is, how can you judge the performance of an algorithm?
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1answer
84 views

Why does increasing the training set size not improve the results?

I have trained a model on a training set, which is not that big (overall around 120 true positives, and of course lots of negative examples). What I am trying to do is to improve the results by ...
3
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1answer
1k views

What is the difference between BatchNorm and Adaptive BatchNorm (AdaBN)?

I understand that BatchNorm (Batch Normalization) centers to (mean, std) = (0, 1) and potentially scales (with $ \gamma $) and offsets (with $ \beta $) the data which is input to the layer. BatchNorm ...
3
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4answers
702 views

Discrepancy between training set and real-world data set: domain adaptation?

I have read in literature that in some cases the training set is not representative for a real-world dataset. However, I cannot seem to find a proper term describing this phenomenon; what is the ...
3
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2answers
809 views

Training data from different sources

I am working on a binary classification problem. My data contains 100K samples from two different sources. When I perform the training and testing on data from the first source I can achieve ...
3
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2answers
453 views

Dealing with an apparently inseparable dataset

I'm attempting to build a model/suite of models to predict a binary target. The exact details of the models aren't important, but suffice to say that I've tried with half a dozen different types of ...