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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...
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Masked self-supervised ECG representation learning via multiview information bottleneck
In recent years, self-supervised learning-based models have been widely used for electrocardiogram (ECG) representation learning. However, most of...
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Dehaze on small-scale datasets via self-supervised learning
Real-world dehazing datasets usually suffer from small scales because of high collection costs. If networks are trained with such insufficient data,...
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Series2vec: similarity-based self-supervised representation learning for time series classification
We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of...
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Self-supervised action representation learning from partial consistency skeleton sequences
In recent years, self-supervised representation learning for skeleton-based action recognition has achieved remarkable results using skeleton...
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Deep learning approaches for lyme disease detection: leveraging progressive resizing and self-supervised learning models
Lyme disease diagnosis poses a significant challenge, with blood tests exhibiting an alarming inaccuracy rate of nearly 60% in detecting early-stage...
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A Review of Predictive and Contrastive Self-supervised Learning for Medical Images
Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the...
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Automated detection of class diagram smells using self-supervised learning
Design smells are symptoms of poorly designed solutions that may result in several maintenance issues. While various approaches, including...
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DisRot: boosting the generalization capability of few-shot learning via knowledge distillation and self-supervised learning
Few-shot learning (FSL) aims to adapt quickly to new categories with limited samples. Despite significant progress in utilizing meta-learning for...
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Trusted 3D self-supervised representation learning with cross-modal settings
Cross-modal setting employing 2D images and 3D point clouds in self-supervised representation learning is proven to be an effective way to enhance...
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Decoupling Anomaly Discrimination and Representation Learning: Self-supervised Learning for Anomaly Detection on Attributed Graph
Anomaly detection on attributed graphs is a crucial topic for practical applications. Existing methods suffer from semantic mixture and imbalance...
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Self-supervised CondenseNet for feature learning to increase the accuracy in image classification
Deep learning methods are leveraged in various computer science and artificial intelligence areas, including image classification. Convolutional...
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HAPiCLR: heuristic attention pixel-level contrastive loss representation learning for self-supervised pretraining
Recent self-supervised contrastive learning methods are powerful and efficient for robust representation learning, pulling semantic features from...
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Context Autoencoder for Self-supervised Representation Learning
We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. We pretrain an...
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Audio Mixing Inversion via Embodied Self-supervised Learning
Audio mixing is a crucial part of music production. For analyzing or recreating audio mixing, it is of great importance to conduct research on...
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Robust self-supervised learning for source-free domain adaptation
Source-free domain adaptation (SFDA) is from unsupervised domain adaptation (UDA) and do apply to the special situation in reality that the source...
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Deep Unpaired Blind Image Super-Resolution Using Self-supervised Learning and Exemplar Distillation
Existing deep blind image super-resolution (SR) methods usually depend on the paired training data, which is difficult to obtain in real...
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Multi-view and multi-augmentation for self-supervised visual representation learning
In the real world, the appearance of identical objects depends on factors as varied as resolution, angle, illumination conditions, and viewing...
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Understanding the limitations of self-supervised learning for tabular anomaly detection
While self-supervised learning has improved anomaly detection in computer vision and natural language processing, it is unclear whether tabular data...
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Multi-view Self-supervised Learning and Multi-scale Feature Fusion for Automatic Speech Recognition
To address the challenges of the poor representation capability and low data utilization rate of end-to-end speech recognition models in deep...
