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About 10,000 papers for “Labeled data”

Labeled data

Kristen Grauman, Work Yong, Jae Lee et al. · 2014

Cited by 0

Kashtanka.pet labeled data

Konstantin Kudelkin, Calvin Tee, Nikolay Arefyev · Zenodo (CERN European Organization for Nuclear Research) · 2022

Data partition from kashtanka.pet

Cited by 0Open access

Labeled vs. unlabeled data.

Han Zhuang (2416624), Tzu-Yang Huang (3106818), Daniel E. Acuna (3158904) · Figshare · 2021

<p>Features of both datasets are nearly identical. The prediction dataset could provide a reasonable estimation of the more general problems of graphical integrity.</p>

Cited by 0Open access

Displaying Labeled Quantitative Data

Marcin Kozak · Social Communication · 2019

The information world is full of labeled quantitative data, in which a number of qualitative cat-egories are to be compared based on a quantitative variable. Their graphical representations arevarious and serve different audiences and purposes. Based on a simple data set and its different vi-sualizations, we will play with the data and their visual representation. We will use well-known charts,such as a regular table, a bar plot, and a word cloud; less-know, such as Cleveland’s dot plot, a fanplot, and a text-table; and new ones, constructed for the very aim of this essay, such as a labeledrectangle plot and a ruler-like graph. Our discussion will not aim to choose the best graph but ratherto show the different faces of visualizing labeled quantitative data. I hope to convince the readers thatit is always worth spending a minute on pondering how to present their data.

Cited by 1Open access

Location of labeled data.

Marjolein Oostrom (11167863), Michael A. Muniak (18275626), Rogene M. Eichler West (18275629) et al. · Figshare · 2024

Cited by 0Open access

Labeled Dendritic Spines - Training Data

Michael S. Smirnov, Tavita Garrett · Figshare · 2018

This is a collection of images of fluorescent spines, each labeled with bounding boxes indicating where the spines are. Each folder includes an image and its relative bounding boxes in a .csv file. Coordinates of spines centers are also redundantly shown included in the text file, along with the pixel per micron scale. Bounding Boxes are in the [x,y,width,length] format. This data is uses as training data in the linked 2018 Smirnov et al. paper.

Cited by 1Open access

Learning from limited labeled data

Lazarou, Michalis · Imperial College Research Computing Service Data Repository · 2023

A fundamental bottleneck of deep learning models that inhibits their use in many real world applications is their reliance on large labeled datasets. This thesis addresses this limitation by proposing several ideas that can improve the performance of deep learning models when the available labeled data is limited. The thesis proposes a novel algorithm for semi-supervised and transductive inference when the labeled data is limited achieving state of the art performance in the process. The proposed algorithm leverages the manifold structure of the labeled and unlabeled data to assign accurate labels to the unlabeled data before a label cleaning module is used to select the most confident pseudo-labeled data to be used as labeled data and augment the labeled dataset. Additionally, in real world situations the unlabeled data may contain noisy images. Using the unnormalized manifold similarity obtained from label propagation along with the label cleaning module, the aforementioned problem can be addressed effectively and filter the noisy data. Moreover, the thesis identifies a limitation of the traditional label propagation algorithm and proposes a novel variant that assigns more accurate labels to the unlabeled data. The idea is to iteratively optimize the positions of the labeled data in the manifold by minimizing a differentiable loss function. The thesis also investigates the performance of deep learning models when the available unlabeled data is class-imbalanced. A novel algorithm is proposed that achieves state of the art performance by combining effectively the two main lines of research in transductive learning for limited labeled data, which are: class centroid refinement and data manifold exploitation. Lastly, in certain applications obtaining data is extremely difficult. A novel generative model is proposed to generate synthetic data and train a classifier on both real and synthetic data.

Cited by 0Open access

Women's Data Labeled Box Plot

Schmitt, Judy · IRL - University of Missouri, St. Louis (University of Missouri–St. Louis) · 2021

FIGURE 2.25. The box plots for the women’s data with detailed labels. (“Women’s Data Labeled Box Plot” by Judy Schmitt is licensed under CC BY-NC-SA 4.0.)

Cited by 0

Learning with Limited Labeled Data

Ouali, Yassine · theses.fr (ABES) · 2023

Depuis ses débuts, l'objectif de l'intelligence artificielle est de concevoir des systèmes capables d'apprendre aussi efficacement que les humains pour résoudre ou aider à résoudre des problèmes difficiles qui nécessitent une certaine forme d'intelligence humaine. La discipline a connu récemment un essor spectaculaire grâce aux réseaux de neurones profonds et ses extensions qui ont montré des performances sur tout un ensemble de tâches jusqu'alors considérées complexes. Cependant, ce paradigme dominant nécessite une grande quantité de données étiquetées, qui sont souvent coûteuses et difficiles à acquérir. Ces données peuvent également contenir des biais cachés et des erreurs d'annotation, ce qui limite l'application de tels systèmes dans de nombreux domaines. Pourtant, les humains font preuve d'une remarquable capacité à apprendre efficacement dans de nouveaux et divers contextes, en tirant en grande parti de leur expérience à s'adapter à de nouveaux cas et acquérir rapidement de nouvelles compétences. Cette divergence soulève une question évidente : pouvons-nous concevoir des systèmes dotés de capacités similaires ? Dans cette thèse, notre objectif est de développer des algorithmes d'apprentissage efficaces avec une quantité limitée d'étiquettes pour résoudre de diverses tâches pour différentes modalités. A cette fin, cette thèse couvre des travaux qui : i) développent des méthodes d'apprentissage pour des paradigmes avec différents degrés de supervision, ii) présentent des résultats pour différentes modalités, notamment l'image et le texte, et iii) qui gèrent différentes tâches.

Cited by 0Open access

Labeled Dendritic Spines - Training Data

Smirnov, Michael, Tavita Garrett · Figshare · 2018

This is a collection of images of fluorescent spines, each labeled with bounding boxes indicating where the spines are. Each folder includes an image and its relative bounding boxes in a .csv file. Coordinates of spines centers are also redundantly shown included in the text file, along with the pixel per micron scale. Bounding Boxes are in the [x,y,width,length] format. This data is uses as training data in the linked 2018 Smirnov et al. paper.

Cited by 1Open access

RenderGAN: Generating Realistic Labeled Data

Leon Sixt, Benjamin Wild, Tim Landgraf · Frontiers in Robotics and AI · 2018

Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g., lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.

Cited by 16Open access

Learning from Partially Labeled Data

Tommi S. Jaakkola, Arthur C. Smith, Martin Szummer et al. · 2002

Classification with partially labeled data involves learning from a few labeled examples as well as a large number of unlabeled examples, and represents a blend of supervised and unsupervised learning. Unlabeled examples provide information about the input domain distribution, but only the labeled examples indicate the actual classification task. The key question is how to improve classification accuracy by linking aspects of the input distribution P (x) to the conditional output distribution P (y|x) of the classifier.

Cited by 0

Creation of sparsely labeled data.

Yohei Momoki (18196229), Akimichi Ichinose (3511655), Keigo Nakamura (3162762) et al. · Figshare · 2024

<p>By analyzing the findings, the bronchopulmonary segments, where the lesion is located, can be found. Also, the coordinate information of the lesion is recorded as annotation data. This information is combined to create localized ground truth data of bronchopulmonary segments. Reprinted under a CC BY license, with permission from Shingo Iwano, original copyright 2024.</p>

Cited by 0Open access

Labeled Pseudo Data for BL2

Eli Carter, Di'Arra Mostella · Figshare · 2021

Labeled pseudo data that I train and test on for the Phys 494 ML class at UTK's final project

Cited by 0Open access