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Uncertainty-Confidence Fused Pseudo-labeling for Graph Neural Networks
Graph Neural Networks (GNNs) have achieved promising performance for semi-supervised graph learning. However, the training of GNNs usually heavily...
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Informative pseudo-labeling for graph neural networks with few labels
Graph neural networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of...
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A semi-supervised medical image classification method based on combined pseudo-labeling and distance metric consistency
In medical image analysis, obtaining high-quality labeled data is expensive, and there is a large amount of unlabeled image data that is not...
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Non-Outlier Pseudo-Labeling for Short Text Clustering
Instance-level correlation and cluster-level discrepancy of data are two crucial aspects of short text clustering. Current deep clustering methods,...
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Pseudo-labeling and clustering-based active learning for imbalanced classification of wafer bin map defects
Wafer bin map (WBM) defect patterns play a crucial role in identifying the root cause of manufacturing defects in the semiconductor industry....
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Myopic Maculopathy Analysis Using Multi-task Learning and Pseudo Labeling
With the advent of deep learning, research has achieved significant success in various fields of ophthalmology, such as diabetic retinopathy...
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PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision
Although deep learning models have demonstrated impressive performance in various biomedical image segmentation tasks, their effectiveness heavily...
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Advancing Weakly-Supervised Audio-Visual Video Parsing via Segment-Wise Pseudo Labeling
The Audio-Visual Video Parsing task aims to identify and temporally localize the events that occur in either or both the audio and visual streams of...
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Improving pseudo-labeling with reliable inter-camera distance encouragement for unsupervised person re-identification
Unsupervised person re-identification (re-ID) aims to train a discriminative model without identity annotations. State-of-the-art methods usually...
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Attentive recurrent adversarial domain adaptation with Top-k pseudo-labeling for time series classification
The key challenge of Unsupervised Domain Adaptation (UDA) for analyzing time series data is to learn domain-invariant representations by capturing...
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Noisy-Consistent Pseudo Labeling Model for Semi-supervised Skin Lesion Classification
Automated classification of skin lesions in dermoscopy images has the potential to significantly improve survival rates and reduce the risk of death...
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AdaptMatch: Adaptive Consistency Regularization for Semi-supervised Learning with Top-k Pseudo-labeling and Contrastive Learning
Semi-supervised learning has been established as a very effective paradigm for utilizing unlabeled data in order to reduce dependency on large...
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Semi-supervised Learning for Fine-Grained Entity Ty** with Mixed Label Smoothing and Pseudo Labeling
Distant supervision (DS) has been proposed to automatically annotate data and achieved significant success in fine-grained entity ty**(FET)....
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Improving Self-supervised Dimensionality Reduction: Exploring Hyperparameters and Pseudo-Labeling Strategies
Dimensionality reduction (DR) is an essential tool for the visualization of high-dimensional data. The recently proposed Self-Supervised Network...
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A Pseudo-labeling Approach to Semi-supervised Organ Segmentation
In this paper, we adopt a “pseudo-labeling” approach to semi-supervised learning based on 50 labeled images and 2000 unlabeled images. This approach...
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PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
In this paper, we delve into two key techniques in Semi-Supervised Object Detection (SSOD), namely pseudo labeling and consistency training. We...
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3D-PL: Domain Adaptive Depth Estimation with 3D-Aware Pseudo-Labeling
For monocular depth estimation, acquiring ground truths for real data is not easy, and thus domain adaptation methods are commonly adopted using the...
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Semi-supervised medical imaging segmentation with soft pseudo-label fusion
Segmentation is an essential task in modern medical imaging analysis. Since the scarcity of labeled pixel-level annotations often limits its wide...
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Multimodal Pseudo-Labeling Under Various Shooting Conditions: Case Study on RGB and IR Images
In recent years, large-scale datasets with accurate labels have been an extremely important factor in the progress of computer vision. One typical...
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Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
The pioneering method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic...