Questions tagged [domain-adaptation]

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The implementation of AdaBN (Adaptive Batch Norm)

I'm currently focusing on statistically adapting domains using AdaIN. For two types of implementation ideas: Type A: Step 1: After training a source model, freeze beta and gamma (i.e., bias and ...
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Domain weights as hyperparameters

Suppose I have data from two different sources, $(X_1, y_1)$ and $(X_2, y_2)$, where $X_1$ and $X_2$ have the same variables but different distributions, and similarly for $y_1$ and $y_2$. I want to ...
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Domain Generalization vs Domain Adaptation

What is the difference between domain generalization and domain adaptation? According to this paper, domain adaptation deals with unlabelled target domain whereas domain generalization can't do it. ...
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What type of domain shift exists in my data?

I am trying to understand what type of shift(s) exist in my problem to get a better grasp. I have a dataset which comprises of a deep neural network's (DNN) runtime latency ($y$), its architecture ($a$...
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Best way to take into account the number of labeled data to be used for semi-supervised classification task

I am having two datasets with features drawn from different distributions. Both datasets contain equal classes for the same task. The thing i am facing is called domain adaptation - so i train a ...
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How to check whether two image datasets come from the same distribution?

In the literature of transfer learning and domain adaptation everyone talks about two datasets having different feature spaces and different distributions. In case of having image datasets, I think I ...
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3 votes
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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 ...
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Synthetic to real image translation

I want to train an object detection model with synthetic data, After testing it on real data, but the model developed based on synthetic data may not be adapted for real data. Therefore, I want to use ...
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2 votes
1 answer
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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 ...
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Meta/ Few-shot Learning for time series regression

I am working on the calibration of low-cost air sensor data (a time series regression problem). My primary focus is to use some meta/ few-shot learning approach to solve this problem with a lesser ...
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1 vote
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296 views

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 ...
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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 ...
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Generalisation accuracy in Domain Adaptation

Usually, in all the Unsupervised Domain Adaptation literature the test accuracy (on the target domain obviously) is calculated on the unlabelled target examples that the model has already seen during ...
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Neural networks for domain/style transformation?

Is there a neural network that can take data from one distribution and transform it so that it looks as if it were from another distribution, given those two distributions are closely related in some ...
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2 votes
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Multiple-domain adaptation vs multi-task learning

I am confused with the definitions of domain adaptation and multi-task learning. I have K datasets, each with the same feature and label space and thus the same learning problem, but with different ...
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Looseness of the definition of domain adaption

I am a bit intrigued about “domain shift” concept. Specifically, in part 5 of the paper “Coupled Generative Adversarial Networks”, it reads We studied the problem of adapting a digit classifier ...
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9 votes
4 answers
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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 ...
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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 ...
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How to integrate expert knowledge to outlier detection algorithms?

Suppose I have a dataset of 20 features, X1, X2..X20. ...
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ML methods for cold start - domain adaptation

Imagine a scenario: You work with credit card transactions and you use ML to assign probabilities to each transaction to be fraudulent or not. You operate in different countries and you have ML models ...
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1 answer
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Testing the scope of application of a logistic regression model

My aim is to assess whether I can apply a logistic regression that was fitted on a sample A (where I have explanatory variables and the outcomes) to a different sample B where I don't know the ...
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Robust machine learning for slightly different class proportions in multiple data sets

Say we have n similar data sets, with the same variables, and outcome labels x and y. In these data sets, domains slightly differ as suggested by the proportion of the minority class x (ranging from 1%...
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Domain knowledge or data dredging?

I have a binary classification problem where I'm required to classify transactions as anomalous or normal (1 or 0 respectively), with anomalies being the rarer instance. With what I know to be true ...
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4 votes
2 answers
2k views

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 ...
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1 vote
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Ratio of training/testing data different to real life?

My company wants to build a model that will be used to predictive conversion that is usually about 2%. However every sample we purchase (converted or unconverted) is expensive. So my question is: How ...
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33 votes
4 answers
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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 ...
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5 votes
3 answers
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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 ...
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32 votes
6 answers
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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 ...
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Can an unstandarized Beta distribution have a negative domain? [duplicate]

(*Question edited for clarification) Does the lower bound of the unstandarized beta distribution always have to be bigger than 0?
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What description fits this this learning scenario, is this a form of active learning?

A system is involved in dialog with a human partner. The system has a model of a problem domain and knows mappings between words an concepts. The human has its own model of the problem domain and ...
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4 votes
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What are some adaptive machine learning techniques that cater for data that may change slightly but is still correct? [closed]

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 ...
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2 votes
2 answers
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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, ...
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4 votes
1 answer
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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 ...
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6 votes
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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 ...
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