10 research outputs found
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
The Graph Convolutional Networks (GCNs) have achieved excellent results in
node classification tasks, but the model's performance at low label rates is
still unsatisfactory. Previous studies in Semi-Supervised Learning (SSL) for
graph have focused on using network predictions to generate soft pseudo-labels
or instructing message propagation, which inevitably contains the incorrect
prediction due to the over-confident in the predictions. Our proposed
Dual-Channel Consistency based Graph Convolutional Networks (DCC-GCN) uses
dual-channel to extract embeddings from node features and topological
structures, and then achieves reliable low-confidence and high-confidence
samples selection based on dual-channel consistency. We further confirmed that
the low-confidence samples obtained based on dual-channel consistency were low
in accuracy, constraining the model's performance. Unlike previous studies
ignoring low-confidence samples, we calibrate the feature embeddings of the
low-confidence samples by using the neighborhood's high-confidence samples. Our
experiments have shown that the DCC-GCN can more accurately distinguish between
low-confidence and high-confidence samples, and can also significantly improve
the accuracy of low-confidence samples. We conducted extensive experiments on
the benchmark datasets and demonstrated that DCC-GCN is significantly better
than state-of-the-art baselines at different label rates.Comment: 25 pages, 7 figures. Submitted to neuco
A Survey on Deep Semi-supervised Learning
Deep semi-supervised learning is a fast-growing field with a range of
practical applications. This paper provides a comprehensive survey on both
fundamentals and recent advances in deep semi-supervised learning methods from
model design perspectives and unsupervised loss functions. We first present a
taxonomy for deep semi-supervised learning that categorizes existing methods,
including deep generative methods, consistency regularization methods,
graph-based methods, pseudo-labeling methods, and hybrid methods. Then we offer
a detailed comparison of these methods in terms of the type of losses,
contributions, and architecture differences. In addition to the past few years'
progress, we further discuss some shortcomings of existing methods and provide
some tentative heuristic solutions for solving these open problems.Comment: 24 pages, 6 figure