556 skills found · Page 7 of 19
HazyResearch / Ukb Cardiac MriWeakly Supervised MRI Series Classification for the UK Biobank
fitushar / WeaklySupervised 3D Classification Of Chest CT Using Aggregated MultiResolution Segmentation FeatureThis Repo contains the updated implementation of our paper "Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131408 (16 March 2020)
Enjia / Spatial Regularization Network In Tensorflowimplementation of “Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification” in Tensorflow
Li-ZK / SCLUDA 2023Supervised Contrastive Learning-Based Unsupervised Domain Adaptation for Hyperspectral Image Classification
yanlirock / RLANSLearning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient
HiitLee / SALNetSALNet: Semi-supervised Few-Shot Text Classification with Attention-based Lexicon Construction
renwang435 / Adni VisualizeSelf-supervised learning for understanding of morphological features in classification of fMRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database
daiquanyu / AdaGCN TKDEThis paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains.
yangxh11 / P2P NetOfficial implementation of "Fine-Grained Object Classification via Self-Supervised Pose Alignment".
ruiyi-zhang / ScPretrainscPretrain: Multi-task self-supervised learning for cell type classification
LirongWu / GraphMixupCode for ECML-PKDD 2022 paper "GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction"
lyhkevin / Graph V NetA Hierarchical Graph V-Net with Semi-supervised Pre-training for Breast Cancer Histology Image Classification" (IEEE TMI)
GeoRSAI / SSOUDAThis repository is the official implementation of "A Self-supervised-driven Open-set Unsupervised Domain Adaptation Method for Optical Remote Sensing Image Scene Classification and Retrieval" (IEEE TGRS 2023).
HenryPengZou / JointMatch[EMNLP 2023] Official Code of "JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification"
yzhan238 / TELEClassThe source code used for paper "TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision", published in WWW 2025.
Fishdrink / SC Net(1) Purpose: A weakly supervised surface defect detection model using image-level labels for simultaneous classification and segmentation. (2) Experiments: run on 4 datasets, including KolektorSDD2(KSDD2), DAGM 1-10, KolektorSDD(KSDD), Severstal Steel, the classification average precision (AP) reaches 96.0%, 100%, 97.1%, 97.7%,respectively. (1)用途：一种使用图像级标签的弱监督表面缺陷检测模型，可同时进行分类和分割。 (2)实验：在4个数据集上实验，包括KolektorSDD2(KSDD2), DAGM 1-10, KolektorSDD(KSDD), Severstal Steel，分类平均精确率(AP)分别达到96.0%, 100%, 97.1%, 97.7%。
datares / Neodglsemi supervised node classification on neo4j database
aniket03 / Self Supervised Bird ClassificationExplores jigsaw puzzles solvinig as pre-text task for fine grained classification for bird species identification (Implemented with pyTorch)
zhanglu-cst / ClassKGWeakly-supervised Text Classification Based on Keyword Graph
Prasanna1991 / LatentMixingThe implementation of "Semi-supervised Medical Image Classification with Global Latent Mixing". [MICCAI2020]
