Contrastive Identification of Covariate Shift in Image Datadoi:10.1109/VIS49827.2021.9623289Matthew L. OlsonThuy-Vy NguyenGaurav DixitNeale RatzlaffWeng-Keen WongMinsuk KahngIEEE Computer SocietyIEEE Visualization
最后,作者们用实验证明了,在遇到更一般的 non-linear setting 与非周期的 covariance shift 时,SGDm算法的周期性震荡现象仍然会出现。这个结果为我们提供了一个全新的方向来理解 non-i.i.d 条件下的算法,以及当前一些常用的算法在这一情形下的鲁棒性的缺失。
To assess the significance of this recent shift in temperature, calibration, and simulation were performed for three cases. In the first case, denoted as “stationary,” the model is calibrated to the observed streamflow with no conditioning on temperature. The simulated distribution is therefore rep...
Covariate shiftConcept driftRobust machine learningClassifier evaluationModel degradationCLASSIFIERADAPTATIONMost machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and the distribution of...
Detecting covariate shift occurrences in advance allows for preventive measures, such as informing the user to adjust the position of the headset or applying specific corrections in new coming data. We used in this study an unsupervised Machine Learning model, the Isolation Forest, to detect ...