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- Underwater-GAN: Underwater Image Restoration via Conditional Generative Adversarial Network107
This paper proposes Underwater-GAN, a conditional generative adversarial network for underwater image restoration, which uses Wasserstein GAN with gradient penalty term as the backbone network and designs the loss function as the sum of the loss of generative adversary network and the perceptual loss.
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- Uncertainty-Driven Dehazing Network65
A novel uncertainty-driven dehazing network (UDN) is proposed that improves the dehazed results by exploiting the relationship between the uncertain and confident representations, and develops an uncertainty- driven self-distillation loss to improve the uncertain representation by transferring the knowledge from the confident one.
- AIM 2019 Challenge on Image Demoireing: Methods and Results57
This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019, and focuses on the proposed solutions and their results.
- Supervised Hebb rule based feature selection for text classification54
Experimental results indicate that HRFS is effective to achieve better performance than the compared methods and also efficient because it can be described in the view of matrix operation to decrease complexity of feature selection.
- NTIRE 2020 Challenge on Image Demoireing: Methods and Results45
This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020, and examines the current state-of-the-art in image and burst image demoireing problems.
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- Research on customer opinion summarization using topic mining and deep neural network27
A novel framework for summarizing customer opinions from product reviews is proposed that identifies grammatically and semantically meaningful phrases which contain product attributes and their corresponding opinions from original product reviews by using grammar rules and the latent Dirichlet allocation (LDA) model.
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- Online ad effectiveness evaluation with a two-stage method using a Gaussian filter and decision tree approach25
A two-stage method based on a Gaussian filter and a decision tree (M-GFDT) that helps in the removal of ineffective online ads as early as possible and achieves high accuracy in predicting effective online ads is proposed.
- Control of phytoplankton by oysters and the consequent impact on nitrogen cycling in a Subtropical Bay21
This study illustrates the influence of massive oysters on pelagic nitrogen cycling in an "oyster-remediated" estuary, providing implications for restoring oyster reefs to mitigate the symptoms of estuarine eutrophication.
- Distance Variance Score: An Efficient Feature Selection Method in Text Classification21
Experimental results indicate that DVS is able to select discriminative features and reduce the sparsity of DTM, which makes it much more efficient than LS.
- Filter feature selection methods for text classification: a review20
This paper aims to provide a comprehensive systematic review of existing filter feature selection methods for text classification by presenting a detailed discussion on mathematical designs, effectiveness and complexity of existing Filter Feature selection methods of different methodologies (super supervised methods, unsupervised methods and hybrid methods).
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- Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study16
Deep learning-reconstructed lumbar spine MRI protocol enabled a 45% reduction in scan time compared with conventional reconstruction, with comparable image quality and detection performance of common degenerative pathologies.
- ERNIE 5.0 Technical Report15
ERNIE 5.0 represents the first production-scale realization of a trillion-parameter unified autoregressive model that supports both multimodal understanding and generation and systematically addresses the challenges of scaling reinforcement learning to unified foundation models.
- Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings14
Experimental results indicate that the outstanding performance of NNFR model than comparative recommendation algorithms on rating predictions and top-k recommendations can still produce high-quality recommendation results in the scenarios of sparse data.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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