Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [domain-adaptation]

33 questions
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4 answers

Maximum Mean Discrepancy (distance distribution)

I have two data sets (source and target data) which follow different distributions. I am using MMD - that is a non-parametric distribution distance - to compute marginal distribution between the source and target data. source data, Xs target data,…
Mahsa
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What is difference between 'transfer learning' and 'domain adaptation'?

Is there any difference between 'transfer learning' and 'domain adaptation'? I don't know about context, but my understanding is that we have some dataset 1 and train on it, after which we have another dataset 2 for which we want to adapt our model…
mrgloom
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Why is Permuted MNIST good for evaluating continual learning models?

While I was reading papers about continual learning, I found that many researchers use permutated MNIST to evaluate their approach. I understand what it is but it is not clear to me why they use it? What I understand is that they were trying to…
Imtk
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6
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1 answer

What are the most popular domain adaptation methods (for transfer learning)?

I understand supervised and unsupervised learning well, and would be able to identify some 'basic' examples of, for example, supervised classifcation as: SVMs Random Forests Logistic Regression These are key works in the field which have lots of…
Bill Cheatham
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regression with constraints

I have some domain knowledge I want to use in a regression problem. Problem statement The dependent variable $y$ is continuous. The independent variables are $x_1$ and $x_2$. Variable $x_1$ is continuous and positive. Variable $x_2$ is…
PolBM
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Learn a mapping between two datasets using Neural Network

I have two matrices $A_1$ of size $N\times K$ and $A_2$ of size $M\times K$ which contain data and every row has a corresponding label $y \in {1, 2, 3}$. I want to learn a mapping between those two matrices $A_1$ and $A_2$. Is it possible to do it…
konstantin
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What are some adaptive machine learning techniques that cater for data that may change slightly but is still correct?

Are there suitable machine learning techniques that may be applied to a continual stream of data and update its models for data that it believes to be different to the most representative case but still correct? If so, under what scenarios would…
mino
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Frustratingly Easy Domain Adaptation

I refer to the paper by called Frustratingly Easy Domain Adaptation (http://www.umiacs.umd.edu/~hal/docs/daume07easyadapt.pdf) where the feature space of both the source and target data are augmented and used as input to a standard learning…
orbital
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Random Forest vs Gradient Boosting out of distribution

I'm working on a classification task where I have data from a certain company for years between 2017 and 2020. Trying to train different models (Random Forest, XgBoost, LightGBM, Catboost, Explainable Boosting Machines) on separate data with one…
Filippo Fedeli
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Using PCA in domain adaptation

In literature, I see people using (Kernelized) Principle Component Analysis, not for feature extraction, but for domain adaptation. In other words, I have data from a source domain and I would like to match different, but similar data from a target…
Armannas
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How to integrate expert knowledge to outlier detection algorithms?

Suppose I have a dataset of 20 features, X1, X2..X20. Say I perform an outlier detection algorithm such as One-class SVM (http://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html) or IsolationForest…
mommomonthewind
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Biased classification because of data from different sites?

Working in neuroscience, we often classify data from different sites. Usually I balance my data for sites - if I have for instance to classify the data for some illness vs. normal health condition, each of the sites the data is recorded at will…
Pugl
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Domain adaptation under covariate shift: estimating density ratio through a classifier

In domain adaptation under covariate shift, one approach is to weight the instances from the source domain by a factor $\frac{p_T(x)}{p_S(x)}$ in the training, where $p_S(x)$ and $p_T(x)$ represent the density of $x$ in the source and target…
Lei Huang
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Why GAN use adversarial MinMax formulation rather than Min formulation?

For generative adversarial neural network, originally Goodfellow used a MinMax formulation as $\text{Min}_D\text{Max}_G \mathbb{E}_{real}logD(x) dx+ \mathbb{E}_{fake}(1-D(G(z)))dz$. As long as the generator $G$ is fixed, the optimal discriminator…
YUAN Zhiri
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Loss values in a Domain Adversarial

I have been using ResNet-50 with Domain-Adversarial network. I observed an oscillation in the loss values from the evaluation as you can see in the figures, this oscillation was not observed when using ResNet-50 without Domain-Adversarial. I am…
Oscar Moreno
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