Search
Search Results
-
When less is more: on the value of “co-training” for semi-supervised software defect predictors
Labeling a module defective or non-defective is an expensive task. Hence, there are often limits on how much-labeled data is available for training....
-
SSGait: enhancing gait recognition via semi-supervised self-supervised learning
Gait recognition is a challenging biometric technology field due to the complexity of integrating static appearance and dynamic movement patterns in...
-
Self-training involving semantic-space finetuning for semi-supervised multi-label document classification
Self-training is an effective solution for semi-supervised learning, in which both labeled and unlabeled data are leveraged for training. However,...
-
Incorporating semantic consistency for improved semi-supervised image captioning
The high labor cost of image captioning datasets limits the application scenarios of image captioning methods. Therefore, the semi-supervised image...
-
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as...
-
Semi-supervised self-training approach for identification of non-referential pronouns and ellipsis in arabic texts
The identification of non-referential pronouns and ellipsis position is crucial for the Anaphora and Ellipsis Resolution task which is necessary for...
-
A survey of class-imbalanced semi-supervised learning
Semi-supervised learning(SSL) can substantially improve the performance of deep neural networks by utilizing unlabeled data when labeled data is...
-
End-to-end semi-supervised approach with modulated object queries for table detection in documents
Table detection, a pivotal task in document analysis, aims to precisely recognize and locate tables within document images. Although deep learning...
-
Tackle balancing constraints in semi-supervised ordinal regression
Semi-supervised ordinal regression (S 2 OR) has been recognized as a valuable technique to improve the performance of the ordinal regression (OR) model...
-
BSRU: boosting semi-supervised regressor through ramp-up unsupervised loss
Semi-supervised regression aims to improve the performance of the learner with the help of unlabeled data. Popular approaches select some unlabeled...
-
Semi-supervised lung nodule detection with adversarial learning
Lung cancer has long posed a severe threat to human life and health, and early detection as well as effective treatment can significantly improve the...
-
Uncertainty-aware graph neural network for semi-supervised diversified recommendation
Graphs are a powerful tool for representing structured and relational data in various domains, including social networks, knowledge graphs, and...
-
Boosting Graph Convolutional Networks with Semi-supervised Training
Graph convolutional networks (GCN) suffer from the over-smoothing problem, which causes most of the current GCN models to be shallow. Shallow GCN can...
-
CISO: Co-iteration semi-supervised learning for visual object detection
Semi-supervised learning offers a solution to the high cost and limited availability of manually labeled samples in supervised learning. In...
-
DISET: a distance based semi-supervised self-training for automated users’ agent activity detection from web access log
Detecting automated users’ agent activities at any web application through users’ web access logs is a challenging issue. Many machines learning...
-
Uncertain region mining semi-supervised object detection
Semi-supervised learning uses a small amount of labeled data to guide the model and a large amount of unlabeled data to improve its performance. Most...
-
Semi-supervised attack detection in industrial control systems with deviation networks and feature selection
With the rapid development of Industry 4.0, the importance of cyber security for industrial control systems has become increasingly prominent. The...
-
Temporal teacher with masked transformers for semi-supervised action proposal generation
By conditioning on unit-level predictions, anchor-free models for action proposal generation have displayed impressive capabilities, such as having a...
-
SemiDocSeg: harnessing semi-supervised learning for document layout analysis
Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table,...
-
Semi-supervised adversarial discriminative domain adaptation
Domain adaptation is a potential method to train a powerful deep neural network across various datasets. More precisely, domain adaptation methods...
