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Auto CNN classifier based on knowledge transferred from self-supervised model
Training with unlabeled datasets using self-supervised models has the edge over training with labeled datasets, reducing human effort, and no need...
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Knowledge-aware reasoning with self-supervised reinforcement learning for explainable recommendation in MOOCs
Explainable recommendation is important but not yet explored in Massive Open Online Courses (MOOCs). Recently, knowledge graph (KG) has achieved...
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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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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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Self-supervised opinion summarization with multi-modal knowledge graph
Multi-modal opinion summarization aims at automatically generating summaries of products or businesses from multi-modal reviews containing text,...
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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...
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Impact of Autotuned Fully Connected Layers on Performance of Self-supervised Models for Image Classification
With the recent advancements of deep learning-based methods in image classification, the requirement of a huge amount of training data is inevitable...
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Self-supervised extracted contrast network for facial expression recognition
Self-supervised Contrastive learning has recently demonstrated significant performance in Facial Expression Recognition (FER). However, existing...
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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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Integrated self-supervised label propagation for label imbalanced sets
Label propagation is an essential graph-based semi-supervised learning algorithm. However, the algorithm has two problems: how to effectively measure...
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Self-supervised Siamese Autoencoders
In contrast to fully-supervised models, self-supervised representation learning only needs a fraction of data to be labeled and often achieves the...
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An ensemble of self-supervised teachers for minimal student model with auto-tuned hyperparameters via improved Bayesian optimization
Due to a growing demand for efficient deep learning models capable of both high performance and reduced costs in terms of computation, model...
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Node and edge dual-masked self-supervised graph representation
Self-supervised graph representation learning has been widely used in many intelligent applications since labeled information can hardly be found in...
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Self-supervised discriminative model prediction for visual tracking
The discriminative model prediction (DiMP) object tracking model is an excellent end-to-end tracking framework and have achieved the best results of...
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Dual-View Self-supervised Co-training for Knowledge Graph Recommendation
Knowledge Graph Recommendation (KGR), which aims to incorporate Knowledge Graphs (KGs) as auxiliary information into recommender systems and...
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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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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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More from Less: Self-supervised Knowledge Distillation for Routine Histopathology Data
Medical imaging technologies are generating increasingly large amounts of high-quality, information-dense data. Despite the progress, practical use...
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Contrastive disentanglement for self-supervised motion style transfer
Motion style transfer, which aims to transfer the style from a source motion to the target while kee** its content, has recently gained...
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Self-supervised approach for diabetic retinopathy severity detection using vision transformer
Diabetic retinopathy (DR) is a diabetic condition that affects vision, despite the great success of supervised learning and Conventional Neural...
