Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications.

Table 3 presents the details of datasets in HoK1v1 task. Spells set to frenzy . Generally, a level of "1" is used for datasets with the "norm" prefix, while a level This distinction indicates varying levels of difficulty. In the Generalization category, "norm_general" and "hard_general," have their corresponding datasets. For example, to sample the "norm_general" dataset, we let the level-1 model fight with level-0, level-542 For example, in the "norm_hero_general" experiment, we directly use the model trained on "norm_medium" dataset only contains the fixed default hero "luban."

For each prediction task, we select features that have a causal influence on the target of prediction.

Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination without sacrificing calibration.