 Getting “Can't open this video” when attempting to open a video
 ADB cutting off most of my input
 Android Canvas draw exist measure to android screen
 Install android game on TV using USB
 Why is my newly built tube amp making a squealing sound and not passing audio?
 Control system error based on differential process value
 How a diode here can remove the carrier signal  FM receiver?
 74LS08 AND gate always outputting high?
 Explain adjustable 4A regulator
 designing a universal 010v and 0300k resistance input circuit
 Material with fairly high resistivity, but allows flow of charge
 Reducing Redundant Calculations
 Why is referential integrity useful and how is it beneficial?
 How to educate developers about avoiding phishinglike features?
 Existing architectures for realtime serverclient sync on a huge treebased data structure where clients subscribe to nodes and subtrees?
 What are the rules for making constructs other than golems?
 How to hide an app from the Launchpad?
 How do I (re)mount missing Firewire drives? Why do they vanish?
 How do I turn off the automatic downloading of iOS updates?
 How to Unlock iPhone 6+
Covariate shift detection
Is there any standard approach for detecting the covariate shift between the training and test data ? This would be useful to validate the assumption that covariate shift exists in my database which contains a few hundred images.
There are methods like the KullbackLeibler divergence model, the WaldWolfowitz test for detecting nonrandomness and covariance shift.
A simple test for quick analysis of covariance test would be to build a machine learning model, where the model is repeatedly tested with inputting training data and the production data.
In case, the model can make out the difference between the training and production datasets then it can be a sign of covariance shift.
Adaptive learning with covariate shiftdetection for motor imagerybased brain–computer interface
http://link.springer.com/article/10.1007/s0050001519375
EWMA model based shiftdetection methods for detecting covariate shifts in nonstationary environments (http://www.scienced

There are methods like the KullbackLeibler divergence model, the WaldWolfowitz test for detecting nonrandomness and covariance shift.
A simple test for quick analysis of covariance test would be to build a machine learning model, where the model is repeatedly tested with inputting training data and the production data.
In case, the model can make out the difference between the training and production datasets then it can be a sign of covariance shift.
20170717 11:51:58 
Adaptive learning with covariate shiftdetection for motor imagerybased brain–computer interface
http://link.springer.com/article/10.1007/s0050001519375
EWMA model based shiftdetection methods for detecting covariate shifts in nonstationary environments (http://www.sciencedirect.com/science/article/pii/S0031320314002878)
20170717 11:58:54 
Here is a simple procedure you can use:
learn a classifier to distinguish between train/test data (using regular X features)
compute the phi correlation coefficient to estimate the quality of the classifier = the separability of the train/test data
set a threshold (e.g. .2) above which you can claim there is a covariate shift (and start looking as corrections)
20170717 12:30:57 
You don't give many clues about what properties of the images you might be considering, but it seems that what you might want to measure is the difference in the distributions of the training and tests sets. A useful place to start would be with the Kullback–Leibler divergence, which is a measure of the difference of two distributions.
20170717 12:46:02 
The problem of covariate shift ultimately results in datasets with different underlying mathematical structure. Now, Manifold Learning estimates a low dimensional representation of highdimensional data thereby revealing the underlying structure. Often Manifold Learning techniques are not projections  therefore, different and more powerful, than standard PCA.
I've used Manifold Learning techniques (for eg: IsoMap, MDS, etc) to visualize (and, if possible, quantify) the "(dis)similarity" between train and test datasets.
20170717 13:03:53