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Found 7 papers, 3 papers with code
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
Moreover, we theoretically establish that the number of distinguishable eigenvalues plays a pivotal role in determining the expressive power of spectral graph neural networks.
Semantic Structure Enhanced Contrastive Adversarial Hash Network for Cross-media Representation Learning
Secondly, for further improving learning ability of implicit cross-media semantic associations, a semantic label association graph is constructed, and the graph convolutional network is utilized to mine the implicit semantic structures, thus guiding learning of discriminative features of different modalities.
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation
In this work, we propose SemiCurv, a semi-supervised learning (SSL) framework for curvilinear structure segmentation that is able to utilize such unlabelled data to reduce the labelling burden.
Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime
Recent studies combined finetuning (FT) from pretrained weights with SSL to mitigate the challenges and claimed superior results in the low-label regime.
Diverse and Consistent Multi-view Networks for Semi-supervised Regression
Label collection is costly in many applications, which poses the need for label-efficient learning.
A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition
Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised learning for HAR, particularly in a challenging scenario with class imbalance problem.
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity
In this paper, we propose ARMOURED, an adversarially robust training method based on semi-supervised learning that consists of two components.
