Learning Spatial-Semantic Relationship for Facial Attribute Recognition with Limited Labeled Data
Computer Vision and Pattern Recognition, 2021 Recent advances in deep learning have demonstrated excellent results for Facial Attribute Recognition (FAR), typically trained with large-scale labeled data.
Y. Shu +5 more
semanticscholar +1 more source
Labeled entities from social media data related to avian influenza disease
Data in Brief, 2022 This dataset is composed by spatial (e.g. location) and thematic (e.g. diseases, symptoms, virus) entities concerning avian influenza in social media (textual) data in English.
Camille Schaeffer +4 more
doaj +1 more source
Gene function prediction using labeled and unlabeled data
BMC Bioinformatics, 2008 Background In general, gene function prediction can be formalized as a classification problem based on machine learning technique. Usually, both labeled positive and negative samples are needed to train the classifier.
Wang Yong +3 more
doaj +1 more source
Automated Quantization and Retraining for Neural Network Models Without Labeled Data
IEEE Access, 2022 Deploying neural network models to edge devices is becoming increasingly popular because such deployment decreases the response time and ensures better data privacy of services.
Kundjanasith Thonglek +7 more
doaj +1 more source
Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets [PDF]
, 2019 Although deep learning pathology diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, they still require a huge amount of well annotated data for training.
Belar, Oihana +7 more
core +1 more source
PADI-web corpus: Labeled textual data in animal health domain
Data in Brief, 2019 Monitoring animal health worldwide, especially the early detection of outbreaks of emerging pathogens, is one of the means of preventing the introduction of infectious diseases in countries (Collier et al., 2008) [3].
Julien Rabatel +2 more
doaj +1 more source
Accurate LC peak boundary detection for ¹⁶O/¹⁸O labeled LC-MS data. [PDF]
PLoS ONE, 2013 In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance.
Jian Cui +7 more
doaj +1 more source
Pixel-Wise Fabric Defect Detection by CNNs Without Labeled Training Data
IEEE Access, 2020 Surface inspection is a necessary process of fabric quality control. However, it remains a challenging task owing to diverse types of defects, various patterns of fabric texture, and application requirements for detection speed.
Zhen Wang, Junfeng Jing
doaj +1 more source
Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition
, 2020 We propose a novel approach to semi-supervised automatic speech recognition (ASR). We first exploit a large amount of unlabeled audio data via representation learning, where we reconstruct a temporal slice of filterbank features from past and future ...
Kirchhoff, Katrin +3 more
core +1 more source
Geostatistical semi-supervised learning for spatial prediction
Artificial Intelligence in Geosciences, 2022 Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms.
Francky Fouedjio, Hassan Talebi
doaj +1 more source