Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks94 from public data
- 245
- Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach239
This work proposes a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic source domains to real target domains and reports state-of-the-art results for the adaptation from both GTAV and SYNTHIA to Cityscapes.
- Can Cross Entropy Loss Be Robust to Label Noise?220
A neural network that learns the relationships among the predictors associated to each class, yielding First-Order Logic (FOL)-based descriptions is proposed, which can integrate human-driven preferences that guide the learning-to-explain process, and it is presented in a unified framework.
- An efficient instance selection algorithm to reconstruct training set for support vector machine148
This paper focuses on the fact of uneven distribution of instances in the vector space to propose an efficient self-adaption instance selection algorithm from the viewpoint of geometry-based method and results show that the proposed algorithm outperforms most of competitive algorithms due to its high efficiency and efficacy.
- 102
- 82
- 78
- Spatiotemporal Variations of Air Pollutants and Ozone Prediction Using Machine Learning Algorithms in Beijing-Tianjin-Hebei Region from 2014 to 202169
A seasonal and diurnal pattern with summer and afternoon peaks was found for ozone, which was contrast with other pollutants, and RF model was beneficial and suggested for predicting spatiotemporal variations of ozone in future studies.
- An Effective Conversation‐Based Botnet Detection Method61
The experimental results show the conversation-based detection approach can identify botnet with higher accuracy and lower false positive rate than flow-based approach.
- 49
- C 2 IMUFS: Complementary and Consensus Learning-Based Incomplete Multi-View Unsupervised Feature Selection45
A complementary and consensus learning-based incomplete multi-view unsupervised feature selection method (IMUFS), which integrates feature selection into an extended weighted non-negative matrix factorization model equipped with adaptive learning of view-weights and a sparse sparse linear combinations of multiple similarity matrices derived from different views.
- A new Centroid-Based Classification model for text categorization43
The proposed gravitation model is proved to be less over-fitting and has higher learning ability than CBC model, which shows that it consistently outperforms CBC together with the Class-Feature-Centroid Classifier (CFC).
- Adaptive graph-based generalized regression model for unsupervised feature selection40
A novel generalized regression model equipped with an uncorrelated constraint and the l 2 , 1 -norm regularization is presented, which can simultaneously select the uncor related and discriminative features as well as reduce the variance of these data points belonging to the same neighborhood, which is helpful for the clustering task.
- 39
- Breast tumor classification through learning from noisy labeled ultrasound images37
This work proposes an effective approach called noise filter network (NF-Net) to address the problem of noisy labels when training breast tumor classification models, and can generate benign/malignant labels according to the BI-RADS ratings.
- TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering36
A novel tiered reasoning method that dynamically selects object level candidates based on language representations and generates robust pairwise relations within the selected candidate objects and achieves state-of-the-art performance on two VQA datasets.
- 33
- Long short‐term memory on abstract syntax tree for SQL injection detection32
A novel detection approach based on long short ‐ term memory and abstract syntax tree is proposed, which could detect SQLIAs from the raw query strings and work under SQL detection bypassing scenario.
- Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey30
This paper provides a comprehensive review of the current deep CPS with a novel taxonomy, including network architectures, level of supervision, and learning paradigm, and discussed deep learning-based CPS methods' main challenges and opportunities.
- Learning cross-domain semantic-visual relationships for transductive zero-shot learning30
The Transferrable Semantic-Visual Relation (TSVR) approach towards transductive ZSL is proposed, which redefines image recognition as predicting the similarity/dissimilarity labels for semantic-visual fusions consisting of class attributes and visual features.
- TarGAN: Generating target data with class labels for unsupervised domain adaptation28
A new unsupervised domain adaptation method named TarGAN is proposed, which can successfully disentangle the class and the style codes of the target generator and generate Target samples with given class labels based on the recent advances of Generative Adversarial Networks.
- 26
- Partial Label Learning with Semantic Label Representations26
A novel framework partial label learning with semantic label representations dubbed ParSE is proposed, which consists of two synergistic processes, including visual-semantic representation learning and powerful label disambiguation.
- 25
- Online COVID-19 diagnosis with chest CT images: Lesion-attention deep neural networks24
A lesion-attention deep neural network (LA-DNN) is developed to predict COVID-19 positive or negative with a richly annotated chest CT image dataset, and the experimental results show that the area under the curve (AUC) and sensitivity for the diagnosis of CO VID-19 patients are 91.2% and 85.7% respectively, which reach the clinical standards for practical use.
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 2026-10-12. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.