Axie infinity is a complicated card game with a huge-scale action space.
The UDA for ordinal classification requires inducing non-trivial ordinal distribution prior to the latent space.
In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w. r. t.
The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk.
On the other end, "classical methods" such as optimal control generate solutions without collecting data, but assume that an accurate model of the system and environment is known and are mostly limited to problems with low-dimensional (lo-dim) state spaces.
However, the cross entropy loss can not take the different importance of each class in an self-driving system into account.
The ground metric of Wasserstein distance can be pre-defined following the experience on a specific task.
This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision.
We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation.
We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images.