Machine learning aims to generalize models trained on given datasets to make accurate predictions or decisions on new, unseen data (El Naqa and Murphy, 2015). The effectiveness of those models depends on the alignment between the training datasets and deployment environments (Quinonero-Candela et al.,2008).

We prove the existence of this geometry for a wide class of real world games by exposing their temporal nature.

We follow the derivation route in [7] except the coordinating weight part. According to Eq.(7), we update θ According to the chain rule, Eq.(15) can be written as: For the right part of Eq.(16), it follows that [ ( Figure 3: The Class Activation Map (CAM) [10] is used to perform visual ablation analysis on a chest x-ray image in Open-i dataset. The background color is blue, with red or yellow representing the disease location. The number on the top left corner of each image is the predicted probability for the corresponding disease. We visualize the domain adaptation performance on the transfer scenario NIH-CXR14, CheXpert, MIMIC-CXR to Open-i. The visualization sample in the Open-i is suffering from Atelecsis and Effusion disease.