LAUSR

About LAUSR
Donate
Contact Us

Follow us:

You are not signed in:

Sign Up!


   Articles with "unlabeled samples" as a keyword



Semi‐supervised multiple empirical kernel learning with pseudo empirical loss and similarity regularization

Sign Up to like & get
recommendations! Published in 2022 at "International Journal of Intelligent Systems"

DOI: 10.1002/int.22690

Abstract: Multiple empirical kernel learning (MEKL) is a scalable and efficient supervised algorithm based on labeled samples. However, there is still a huge amount of unlabeled samples in the real‐world application, which are not applicable for… read more here.

Keywords: labeled samples; empirical kernel; unlabeled samples; kernel learning ... See more keywords

Optical sectioning of unlabeled samples using bright-field microscopy

Sign Up to like & get
recommendations! Published in 2022 at "Proceedings of the National Academy of Sciences of the United States of America"

DOI: 10.1073/pnas.2122937119

Abstract: The bright-field (BF) optical microscope is a traditional bioimaging tool that has been recently tested for depth discrimination during evaluation of specimen morphology; however, existing approaches require dedicated instrumentation or extensive computer modeling. We report… read more here.

Keywords: bright field; microscopy; sectioning unlabeled; optical sectioning ... See more keywords

Learning From Reliable Unlabeled Samples for Semi-Supervised SAR ATR

Sign Up to like & get
recommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"

DOI: 10.1109/lgrs.2022.3197892

Abstract: Synthetic aperture radar automatic target recognition (SAR ATR) has been suffering from the insufficient labeled samples as the annotation of SAR data is time-consuming. Thus, adding unlabeled samples into training has attracted the attention of… read more here.

Keywords: supervised sar; semi supervised; unlabeled samples; sar atr ... See more keywords

Semisupervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework

Sign Up to like & get
recommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2022.3195924

Abstract: Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI) classification when abundant labeled samples are available. The problem is that HSI sample annotation is extremely costly and the budget for this task is… read more here.

Keywords: classification; framework; pseudo; unlabeled samples ... See more keywords

Dual-Stream Class-Adaptive Network for Semi-Supervised Hyperspectral Image Classification

Sign Up to like & get
recommendations! Published in 2024 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2024.3357455

Abstract: Semi-supervised classification of remote sensing (RS) hyperspectral image (HSI) aims at exploiting both labeled and unlabeled samples for accurate land cover recognition. However, imbalanced data distribution and different classification difficulties negatively affect classification performance. Focused… read more here.

Keywords: classification; class; semi supervised; class adaptive ... See more keywords

An Enhanced Adaptive Confidence Margin for Semi-Supervised Facial Expression Recognition.

Sign Up to like & get
recommendations! Published in 2025 at "IEEE transactions on pattern analysis and machine intelligence"

DOI: 10.1109/tpami.2025.3612953

Abstract: Semi-supervised learning (SSL) provides a practical framework for leveraging massive unlabeled samples, especially when labels are expensive for facial expression recognition (FER). Typical SSL methods like FixMatch select unlabeled samples with confidence scores above a… read more here.

Keywords: facial expression; semi supervised; confidence; margin ... See more keywords

Contrastive Learning for Semi-Supervised Deep Regression With Generalized Ordinal Rankings From Spectral Seriation.

Sign Up to like & get
recommendations! Published in 2025 at "IEEE transactions on pattern analysis and machine intelligence"

DOI: 10.1109/tpami.2025.3635904

Abstract: Contrastive learning methods enforce label distance relationships in feature space to improve representation capability for regression models. However, these methods highly depend on label information to correctly recover ordinal relationships of features, limiting their applications… read more here.

Keywords: ordinal rankings; spectral seriation; semi supervised; regression ... See more keywords

Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples

Sign Up to like & get
recommendations! Published in 2022 at "IEEE Transactions on Signal Processing"

DOI: 10.1109/tsp.2023.3256275

Abstract: The problem of quickest anomaly detection in networks with unlabeled samples is studied. At some unknown time, an anomaly emerges in the network and changes the data-generating distribution of some unknown sensor. The data vector… read more here.

Keywords: detection; anomaly detection; unlabeled samples; quickest anomaly ... See more keywords

Research on the Guidance of Youth Labor Education Based on the “Combination of Education and Production Labor” Program Based on the Deep Learning Model

Sign Up to like & get
recommendations! Published in 2022 at "Computational Intelligence and Neuroscience"

DOI: 10.1155/2022/2576559

Abstract: At present, there is a lack of research on Marx's idea of “combining education and productive labor” and its guiding significance for youth labor education, and no effective teaching model has been formed. In response… read more here.

Keywords: labor education; education; model; unlabeled samples ... See more keywords