Graph representation learning is an important task with applications in various areas such as online social networks, e-commerce networks, WWW, and semantic webs.
Although the basic neural network provides an alternative approach for structural dynamics analysis, the lack of physics law inside the neural network limits the model accuracy and fidelity.
The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide graph structure information for a model $f(X)$.
Second, a clothes warping module warps clothes image according to the generated semantic layout, where a second-order difference constraint is introduced to stabilize the warping process during training. Third, an inpainting module for content fusion integrates all information (e. g. reference image, semantic layout, warped clothes) to adaptively produce each semantic part of human body.
On the technical side, the Partial-Residual GCN takes the position features of the branches, with the 3D spatial image features as conditions, to predict the label for each branches.
First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.
In this paper, we propose self-enhanced GNN (SEG), which improves the quality of the input data using the outputs of existing GNN models for better performance on semi-supervised node classification.
Edit-distance-based string similarity search has many applications such as spell correction, data de-duplication, and sequence alignment.
In particular, at the high compression ratio end, HSQ provides a low per-iteration communication cost of $O(\log d)$, which is favorable for federated learning.