Papers Data Visualization
“Data Visualization” 태그가 달린 논문 335편 · 필터 해제
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding o…
Contrastive LearningData AugmentationData VisualizationOut-of-Distribution GeneralizationYouTube Ad View Sentiment Analysis using Deep Learning and Machine Learning
Sentiment Analysis is currently a vital area of research. With the advancement in the use of the internet, the creation of social media, websites, blogs, opinions, ratings, etc. has increased rapidly. People express thei…
BIG-bench Machine LearningData VisualizationSentiment AnalysisIoT- Based Low-Cost Soil Moisture and Soil Temperature Monitoring System
Soil moisture (SM) is referred to as a finite amount of water molecules within the pore spaces and it is a crucial parameter of Hydro-Meteorological processes. The behaviour of soil moisture water changes spatially and t…
Data VisualizationClassifying Human Activities using Machine Learning and Deep Learning Techniques
Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smar…
Activity RecognitionBIG-bench Machine LearningData VisualizationDeep Learning+3The Kernelized Taylor Diagram
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is used to vis…
Data VisualizationA unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE)
We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show that they can both be derived from a common mathematical framework.…
Data VisualizationResnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset
This residual network has been a broad domain of research in deep learning. Many complex architectures are based upon residual networks. Residual networks are efficient due to skip connections. This paper highlights the…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+14AugStatic - A Light-Weight Image Augmentation Library
The rapid exponential increase in the data led to an abrupt mix of various data types, leading to a deficiency of helpful information. Creating new data with the existing different types of data are presented in this pap…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+13Augmented Balanced Image Dataset Generator Using AugStatic Library
The mixed data consists of various structured and unstructured data. The exponential boom of the amount of data has made the datasets of varying samples. This paper focuses on the image dataset generator that balances an…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+13Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
The data in the real world consists of various kinds of painful features. A majorly found one is the class imbalance in which the number of examples in different classes in a dataset is unequal. The class imbalance is be…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+13Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
although best-in-class AI can deliver extraordinary outcomes in experimentation, data scientists struggle to duplicate these outcomes on actual-world data. It's nothing unexpected-actual data mirrors the messy world that…
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+13Deep PCB To COCO Convertor
Millions of datasets and many models use the input datasets in COCO format. In this paper, we are converting the Deep PCB dataset to COCO format. The Deep PCB is a manufacturing defect data set. It has 1500 image pairs. …
ClassificationData AugmentationData VisualizationDetecting Image Manipulation+15A Tour of Visualization Techniques for Computer Vision Datasets
We survey a number of data visualization techniques for analyzing Computer Vision (CV) datasets. These techniques help us understand properties and latent patterns in such data, by applying dataset-level analysis. We pre…
Data VisualizationSurveyPosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research
There has been significant progress in machine learning algorithms for human pose estimation that may provide immense value in rehabilitation and movement sciences. However, there remain several challenges to routine use…
Data VisualizationKeypoint DetectionManagementPose EstimationSignature and Log-signature for the Study of Empirical Distributions Generated with GANs
In this paper, we bring forward the use of the recently developed Signature Transform as a way to measure the similarity between image distributions and provide detailed acquaintance and extensive evaluations. We are the…
CPUData VisualizationGPUImage Generation+2Smart Tracking Tray System for A Smart and Sustainable Wet Lab Community
The laboratories and research institutes are the major places for cutting-edge scientific exploration. Hundreds of millions of research papers were formed from front-line labs. Behind this glorious achievement were unsus…
Data VisualizationExperimental DesignLearning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach
We propose a kernel-spectral embedding algorithm for learning low-dimensional nonlinear structures from high-dimensional and noisy observations, where the datasets are assumed to be sampled from an intrinsically low-dime…
Data VisualizationParametric t-Stochastic Neighbor Embedding With Quantum Neural Network
t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensi…
BIG-bench Machine LearningData VisualizationQuantum Machine LearningDSSIM: a structural similarity index for floating-point data
Data visualization is a critical component in terms of interacting with floating-point output data from large model simulation codes. Indeed, postprocessing analysis workflows on simulation data often generate a large nu…
Data VisualizationImage Quality AssessmentSSIMTopology-Preserving Dimensionality Reduction via Interleaving Optimization
Dimensionality reduction techniques are powerful tools for data preprocessing and visualization which typically come with few guarantees concerning the topological correctness of an embedding. The interleaving distance b…
Data VisualizationDimensionality Reduction