Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features.
Specifically, we propose a novel alignment loss term that minimizes the kernel Bures-Wasserstein distance between the joint distributions.
As a fundamental problem in machine learning, dataset shift induces a paradigm to learn and transfer knowledge under changing environment.
Also, to strengthen characterization on the capillaries and the edges of blood vessels, we define a residual pyramid architecture which decomposes the spatial information in the decoding phase.
Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i. e., class centers) in a hyper-sphere manifold.
Previous UDA methods assume that the source and target domains share an identical label space, which is unrealistic in practice since the label information of the target domain is agnostic.
KLN can simultaneously learn a more expressive kernel and label prediction distribution, thus, it can be used to improve the classification performance in both supervised and semi-supervised learning scenarios.
By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders.
Second, batch-wise training of deep learning limits the characterization of the global structure.
Consequently, the crucial point of image set recognition is to mine the intrinsic connection or structural information from the image batches with variations.
In this paper, we consider a more general application scenario where the label distributions of the source and target domains are not the same.
Second, the batch-wise training manner in deep learning limits the description of the global structure.
We derive a camera style adaptation framework to learn the style-based mappings between different camera views, from the target domain to the source domain, and then we can transfer the identity-based distribution from the source domain to the target domain on the camera level.