Specifically, it complements either the edge label information or the structural information which Graph2vec misses with the embeddings of the line graphs.
It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few labeled data could not accurately represent the true data distribution.
In this paper, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech.
To demonstrate this ambiguity, we construct a modality selector (or disambiguator) network, and this model gets substantially lower accuracy on our challenge set, compared to existing datasets, indicating that our questions are more ambiguous.
The optimal binning is the optimal discretization of a variable into bins given a discrete or continuous numeric target.
Minimum DFS codes are canonical labels and capture the graph structure precisely along with the label information.
In this study, a perceptually hidden object-recognition method is investigated to generate secure images recognizable by humans but not machines.
To demonstrate the benefits of our approach, we show that lesion detectors trained on our harvested lesions can significantly outperform the same variants only trained on the original annotations, with boost of average precision of 7% to 10%.
Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations.
We propose the first fast and certifiable algorithm for the registration of two sets of 3D points in the presence of large amounts of outlier correspondences.