Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain.
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.
Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain.
In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network.
Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids.
However, the cross entropy loss can not take the different importance of each class in an self-driving system into account.
In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possibly extract identity factors from the I frame with a pre-trained face recognition network.
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 propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining ($i. e.,$ using arc length of a circle) or adaptively learning the ground metric.
The importance of each image is usually considered either equal or based on a quality assessment of that image independent of other images and/or videos in that image set.
We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images.
This paper targets the problem of image set-based face verification and identification.
For the former, we directly apply a CCN to the binarized representation of an image to compute the Bernoulli distribution of each code for entropy estimation.
In this paper, we present correlated logistic (CorrLog) model for multilabel image classification.