Survey on leveraging pre-trained generative adversarial networks for image editing and restoration
Generative adversarial networks (GANs) have drawn enormous attention due to their simple
yet effective training mechanism and superior image generation quality. With the ability to …
yet effective training mechanism and superior image generation quality. With the ability to …
A comprehensive review of deep learning-based real-world image restoration
Real-world imagery does not always exhibit good visibility and clean content, but often
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …
[PDF][PDF] Graph Debiased Contrastive Learning with Joint Representation Clustering.
By contrasting positive-negative counterparts, graph contrastive learning has become a
prominent technique for unsupervised graph representation learning. However, existing …
prominent technique for unsupervised graph representation learning. However, existing …
Deal: An unsupervised domain adaptive framework for graph-level classification
Graph neural networks (GNNs) have achieved state-of-the-art results on graph classification
tasks. They have been primarily studied in cases of supervised end-to-end training, which …
tasks. They have been primarily studied in cases of supervised end-to-end training, which …
Unsupervised structure-adaptive graph contrastive learning
Graph contrastive learning, which to date has always been guided by node features and
fixed-intrinsic structures, has become a prominent technique for unsupervised graph …
fixed-intrinsic structures, has become a prominent technique for unsupervised graph …
Margin preserving self-paced contrastive learning towards domain adaptation for medical image segmentation
To bridge the gap between the source and target domains in unsupervised domain
adaptation (UDA), the most common strategy puts focus on matching the marginal …
adaptation (UDA), the most common strategy puts focus on matching the marginal …
Multi-scale variational graph autoencoder for link prediction
Z Guo, F Wang, K Yao, J Liang, Z Wang - Proceedings of the Fifteenth …, 2022 - dl.acm.org
Link prediction has become a significant research problem in deep learning, and the graph-
based autoencoder model is one of the most important methods to solve it. The existing …
based autoencoder model is one of the most important methods to solve it. The existing …
Unsupervised hyperbolic representation learning via message passing auto-encoders
Most of the existing literature regarding hyperbolic embedding concentrate upon supervised
learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In …
learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In …
Eliciting structural and semantic global knowledge in unsupervised graph contrastive learning
Abstract Graph Contrastive Learning (GCL) has recently drawn much research interest for
learning generalizable node representations in a self-supervised manner. In general, the …
learning generalizable node representations in a self-supervised manner. In general, the …
X-ggm: Graph generative modeling for out-of-distribution generalization in visual question answering
Encouraging progress has been made towards Visual Question Answering (VQA) in recent
years, but it is still challenging to enable VQA models to adaptively generalize to out-of …
years, but it is still challenging to enable VQA models to adaptively generalize to out-of …