cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57702/0eqju666
title: GEBO

description: The dataset used in this paper is a mixed-space optimization problem, where the inputs contain a mix of discrete and continuous variables.
DOI: 10.57702/rrorx3ke
title: Func2C

description: The dataset used in this paper is a synthetic task with 2 discrete variables and 2 continuous variables.
DOI: 10.57702/y4gpoj4o
title: Neural Network HPO

description: The dataset used in this paper is a real-world task for hyperparameter optimization of a neural network.
DOI: 10.57702/5thyyogy
title: Implicit Regularization of SGD with Preconditioning for Least Square Problems

description: The dataset used in the paper is a least squares regression problem instance.
DOI: 10.57702/lgn0xnti
title: Baidu Search

description: Online human behavior data is collected from Baidu Search. Human preference data is collected for reward modeling.
DOI: 10.57702/xivstw8i
title: DiDi GAIA Open Dataset

description: The dataset is extracted from the DiDi GAIA Open Dataset, which provides OD information of matched orders and completed trips from Nov. 1st to Nov. 31st, 2016, in Chengdu, China.
DOI: 10.57702/0w9n2vr4
title: RL for Consistency Models: Faster Reward Guided Text-to-Image Generation

description: The dataset used in this paper for text-to-image generation tasks.
DOI: 10.57702/y5604wxl
title: GIU-GANs:Global Information Utilization for Generative Adversarial Networks

description: Recently, with the rapid development of artificial intelligence, image generation based on deep learning has advanced significantly. Image generation based on Generative Adversarial Networks (GANs) is a promising study. However, because convolutions are limited by spatial-agnostic and channel-speciﬁc, features extracted by conventional GANs based on convolution are constrained. There-fore, GANs cannot capture in-depth details per image.
DOI: 10.57702/nz6j9i3z
title: Bapst et al. dataset

description: The dataset built by Bapst et al. in It is obtained from molecular dynamics simulations of an 80:20 Kob-Andersen mixture of N = 4096 particles in a three-dimensional box with periodic boundary conditions, at number densities of ρ ≃ 1.2.
DOI: 10.57702/xq6rz9pv
title: Shiba et al. dataset

description: The dataset used in Shiba et al. 2022, Botan: Bond targeting network for prediction of slow glassy dynamics by machine learning relative motion.
DOI: 10.57702/czdkdzvd
title: Clothing1M

description: Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade the generalization of models and result in memorizing samples, further learning erroneous associations of data contents to incorrect annotations.
DOI: 10.57702/r21dylur
title: CySGAN: Cyclic Segmentation Generative Adversarial Network

description: Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying a pre-trained model optimized on diverse training data or conducting domain translation and image segmentation as two independent steps.
DOI: 10.57702/eknekwiv
title: Cloud Detection Method Fusing Multi-Scale Spectral and Spatial Features

description: A lightweight deep learning-based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features
DOI: 10.57702/d8aecdfc
title: WHUS2-CD

description: A new Sentinel-2 cloud detection dataset (WHUS2-CD) containing 32 Sentinel-2A images
DOI: 10.57702/taix3qsd
title: OneFormer

description: OneFormer is a universal image segmentation framework jointly trained on the panoptic, semantic, and instance segmentation tasks.
DOI: 10.57702/pomnivcs
title: Ref-Youtube-VOS

description: Ref-Youtube-VOS is an extensive referring video object segmentation dataset that comprises approximately 15,000 referring expressions associated with more than 3,900 videos.
DOI: 10.57702/btd5y8yu
title: ISIC skin cancer segmentation challenge and lung segmentation dataset

description: Skin cancer segmentation and lung lesion segmentation datasets
DOI: 10.57702/7td5thbx
title: Finding and measuring lungs in CT data

description: Finding and measuring lungs in CT data
DOI: 10.57702/ug0166e1
title: BanglaLekha Isolated Dataset

description: The dataset Im going to be using for this problem is the BanglaLekha Isolated Dataset.
DOI: 10.57702/h2zvm327
title: CF-NeRF

description: A novel end-to-end method that does not require prior camera parameters to deal with image sequences with complex trajectories.
DOI: 10.57702/8f92rakn
title: Economy Estimation of Mainland China at County-Level Based on Landsat Images

description: Dataset for economy estimation of mainland China at county-level based on Landsat images
DOI: 10.57702/9ghct2d0
title: Spatial analysis of global urban extent from DMSP-OLS nighttime lights

description: Global urban extent dataset from DMSP-OLS nighttime lights
DOI: 10.57702/99dhyk4f
title: WIKIDISTANT

description: A dataset for relation extraction, containing 454 target relation categories, including the negative relation.
DOI: 10.57702/f1975jty
title: NYT10

description: A dataset for relation extraction, containing 53 relation categories, including a negative relation.
DOI: 10.57702/zjngeec4
title: DRAIN: A Deep Learning Approach to Rain Retrieval from GPM Passive Microwave Radiometer

description: Rain retrieval algorithm DRAIN using deep learning techniques
DOI: 10.57702/yow9utko
title: International Global Precipitation Measurement (GPM) Program and Mission: An Overview

description: Global Precipitation Measurement (GPM) dataset
DOI: 10.57702/hqb9bp86
title: The New French Operational Polarimetric Radar Rainfall Rate Product

description: Météo-France operational polarimetric radar rainfall rate product
DOI: 10.57702/dsn18umv
title: Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks

description: Global Precipitation Measurement (GPM) dataset
DOI: 10.57702/t7inm7vc
title: COVID-19 Research Articles Classification

description: The dataset used for text classification to support Epistemonikos' effort to filter and categorize research articles related to COVID-19.
DOI: 10.57702/isezgsce
title: Dynamic Approach for Lane Detection using Google Street View and CNN

description: A dataset of 2000 RGB images for lane detection using SegNet architecture.
DOI: 10.5281/zenodo.14261477
title: Dataset of the publication: Sublimable complexes with spin switching: Chemical design, processing as thin films and integration in graphene-based devices

description: Dataset of the publication: Sublimable complexes with spin switching: Chemical design, processing as thin films and integration in graphene-based devices

DOI: 10.1039/d2tc04120k

M. Guevara-Edo, F. J. Valverde-Muñoz, R. Cordoba, M. C. Muñoz, J. Herrero-Martín, J. A. Real, E. Coronado

J. Mater. Chem. C, 11, 24, 8107-8120 (2023)
DOI: 10.5281/zenodo.14261478
title: Dataset of the publication: Sublimable complexes with spin switching: Chemical design, processing as thin films and integration in graphene-based devices

description: Dataset of the publication: Sublimable complexes with spin switching: Chemical design, processing as thin films and integration in graphene-based devices

DOI: 10.1039/d2tc04120k

M. Guevara-Edo, F. J. Valverde-Muñoz, R. Cordoba, M. C. Muñoz, J. Herrero-Martín, J. A. Real, E. Coronado

J. Mater. Chem. C, 11, 24, 8107-8120 (2023)
DOI: 10.57702/8mr7t71u
title: MNIST, Fashion-MNIST, CIFAR-10, and CelebA

description: The dataset used in the paper is not explicitly described, but it is mentioned that the authors pre-trained GANs on four datasets: MNIST, Fashion-MNIST, CIFAR-10, and CelebA.
DOI: 10.57702/pzaybeiv
title: JutePestDetect

description: A comprehensive jute pest dataset consisting of 17 distinct pest classes, facilitating a more thorough analysis.
DOI: 10.57702/y1fq9iuk
title: Photorealistic text-to-image diffusion models with deep language understanding

description: The authors present a photorealistic text-to-image diffusion model with deep language understanding.
DOI: 10.57702/4vhyobil
title: CS1 Course Dataset

description: The dataset used in this paper is a collection of student code submissions for an introductory programming course. The dataset includes 6783 student submissions across three assignments.
DOI: 10.57702/i4xpnvvh
title: RSNA dataset

description: The RSNA dataset contains 26,684 frontal-view chest X-ray images, all of which are labeled as either 0 (no pneumonia) or 1 (pneumonia).
DOI: 10.57702/elgm69uc
title: NIH dataset

description: The NIH dataset contains 14 different diseases, including pneumonia, and is used for training and testing machine learning models.
DOI: 10.57702/0ibg6xtp
title: RUGBY: Players Performance Analysis

description: RUGBY: Players Performance Analysis dataset.
DOI: 10.57702/mf1o4g46
title: PROPELLER: Flow Simulation Exploration

description: PROPELLER: Flow Simulation Exploration dataset.
DOI: 10.57702/n1pp7l3j
title: PARTICLES: 3D Particles in a Turbulent Flow

description: PARTICLES: 3D Particles in a Turbulent Flow dataset.
DOI: 10.57702/b5okdj1g
title: TRAFFIC: Trafﬁc Flow Simulation

description: TRAFFIC: Trafﬁc Flow Simulation dataset.
DOI: 10.57702/d7ns039u
title: DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks

description: Propose an action recognition framework using Generative Adversarial Networks. Our model involves training a deep convolutional generative adversarial network (DCGAN) using a large video activity dataset without label information.
DOI: 10.57702/m3r9ceh7
title: MV-VTON: Multi-View Virtual Try-On with Diffusion Models

description: The proposed method for Multi-View Virtual Try-On (MV-VTON) task, which aims at using the frontal and back clothing to reconstruct the dressing results of a person from multiple views.
DOI: 10.57702/jyr0p3xx
title: Sentinel-1-based AGB regression models

description: This dataset is used for forest AGB prediction in Tanzania. It includes Sentinel-1 data, ALS data, and ground reference data.
DOI: 10.57702/imanwznn
title: ALS-based AGB regression models

description: This dataset is used for forest AGB prediction in Tanzania. It includes Sentinel-1 data, ALS data, and ground reference data.
DOI: 10.57702/pka0nhhi
title: Ground reference data

description: This dataset is used for forest AGB prediction in Tanzania. It includes Sentinel-1 data, ALS data, and ground reference data.
DOI: 10.57702/zstdb3v7
title: Breast Cancer Wisconsin (Original) dataset

description: The dataset used in the paper is the Breast Cancer Wisconsin (Original) dataset, which contains 699 entries, 9 dimensions, and 2 classes.
DOI: 10.57702/gewxlu42
title: MS COCO, Cityscapes, and CTW1500

description: The dataset used in the paper is MS COCO, Cityscapes, and CTW1500. The authors used these datasets for instance segmentation and few-shot instance segmentation tasks.
DOI: 10.57702/ym713290
title: HBCU dataset

description: The HBCU dataset is a public dataset for engagement detection from videos. It contains 120 videos and 34 subjects.
DOI: 10.57702/zng5vrp1
title: MultiRels

description: A multiple relations scene graph-image paired dataset with highly precise labels called MultiRels.
DOI: 10.57702/34vuli9f
title: Face Morphing versus Face Averaging: Vulnerability and Detection

description: Face morphing versus face averaging: Vulnerability and detection.
DOI: 10.57702/gugnt9lp
title: Stack-Exchange-Paired dataset

description: The Stack-Exchange-Paired dataset contains questions and answers from the Stack Overflow dataset, where answers with more votes are preferred.
DOI: 10.57702/ex3feac1
title: IMDB-RLHF-Pair dataset

description: The IMDB-RLHF-Pair dataset is generated by IMDB, and responses with positive sentiment are preferred.
DOI: 10.57702/je63x0fv
title: AFHQ-Dog

description: The dataset used in the paper is not explicitly mentioned, but it is mentioned that the authors used the AFHQ-Dog dataset for dog generation.
DOI: 10.57702/kob379ex
title: VQAv2

description: Visual Question Answering (VQA) has achieved great success thanks to the fast development of deep neural networks (DNN). On the other hand, the data augmentation, as one of the major tricks for DNN, has been widely used in many computer vision tasks.
DOI: 10.57702/zb0xf43h
title: RECLIP: Resource-efficient CLIP by Training with Small Images

description: A simple method that minimizes computational resource footprint for CLIP (Contrastive Language Image Pretraining).
DOI: 10.57702/ngkxf46g
title: LVIS: A dataset for open-vocabulary object detection

description: A dataset for open-vocabulary object detection.
DOI: 10.57702/xrztn3pu
title: Loopitr: Combining dual and cross encoder architectures for image-text retrieval

description: A method for combining dual and cross encoder architectures for image-text retrieval.
DOI: 10.57702/sd1orh6d
title: Learning to prompt for vision-language models

description: A method for learning to prompt for vision-language models.
DOI: 10.57702/hq18jrns
title: Cora, Citeseer, PubMed, Ogbn-arxiv, Amazon-Computer, Amazon-Photo

description: Graph classification and node classification datasets
DOI: 10.57702/gjoqai2n
title: IMDB-B, IMDB-M, REDDIT-B, COLLAB

description: Graph classification and node classification datasets
DOI: 10.57702/rggiuxab
title: MUTAG, PROTEINS, GraphMAE

description: Graph classification and node classification datasets
DOI: 10.57702/63h3e33e
title: Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classiﬁcation

description: A breast cancer malignancy classification dataset using dynamic contrast-enhanced MRI images
DOI: 10.57702/rr83pv8a
title: CrossDocked 2022

description: CrossDocked 2022 dataset for structure-based virtual screening, used for benchmarking docking methods.
DOI: 10.57702/qp6job0i
title: The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking

description: The unmanned aerial vehicle benchmark: Object detection and tracking.
DOI: 10.57702/kurl710i
title: SRDTI: Deep learning-based super-resolution for diffusion tensor MRI

description: High-resolution diffusion-weighted images (DWIs) from low-resolution DWIs using a deep learning-based super-resolution method entitled “SRDTI” to synthesize high-resolution diffusion-weighted images (DWIs) from low-resolution DWIs.
DOI: 10.57702/7n18r8kf
title: MuraNet: Multi-task Floor Plan Recognition with Relation Attention

description: The recognition of information in floor plan data requires the use of detection and segmentation models. However, relying on several single-task models can result in ineffective utilization of relevant information when there are multiple tasks present simultaneously.
DOI: 10.57702/d9ntlkx3
title: Multi Visual Modality Fall Detection Dataset (MUVIM)

description: The Multi Visual Modality Fall Detection Dataset (MUVIM) was used for anomaly detection of falls. It contains (6) vision-based sensors of different modalities including thermal, depth, infra-red (IR) and RGB cameras.
DOI: 10.57702/vvxkzpfs
title: Local Light Field Fusion (LLFF) dataset

description: This dataset is used for novel view synthesis and scene reconstruction.
DOI: 10.57702/cuzbf81s
title: Density-Aware NeRF Ensembles (DANE) dataset

description: This dataset is used for density-aware NeRF ensembles.
DOI: 10.57702/ienxz5pp
title: AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification

description: The proposed framework for image classification tasks, using a hypersphere representation of deep features.
DOI: 10.57702/ahi2x8gr
title: Dual DETRs for Multi-Label Temporal Action Detection

description: Temporal Action Detection (TAD) aims to identify the action boundaries and the corresponding category within untrimmed videos.
DOI: 10.57702/cf3m50ud
title: THUMOS Challenge

description: The THUMOS Challenge dataset contains 413 sports videos of 65 classes.
DOI: 10.57702/4x8drpej
title: Every Moment Counts

description: The Every Moment Counts dataset contains 1,000 hours of video footage of various activities.
DOI: 10.57702/hmmlurj7
title: PredatorPrey

description: The dataset used in the paper is a multi-agent environment, where agents need to communicate with each other to achieve a common goal.
DOI: 10.57702/emvdxq39
title: TrafﬁcJunction

description: The dataset used in the paper is a multi-agent environment, where agents need to communicate with each other to achieve a common goal.
DOI: 10.57702/kwzxhm92
title: Level-based Foraging

description: The dataset used in the paper is a multi-agent environment, where agents need to communicate with each other to achieve a common goal.
DOI: 10.57702/6btlq5qn
title: Onco-Retriever: Generative Classifier for Retrieval of EHR Records in Oncology

description: The dataset is used for training and evaluating the Onco-Retriever model, a generative classifier for retrieval of EHR records in oncology.
DOI: 10.57702/lk331fw2
title: MS MARCO: A Human-Generated Machine Reading Comprehension Dataset

description: The dataset is used for training and evaluating the MS MARCO model, a question answering model.
DOI: 10.57702/k3watrca
title: RBI

description: The RBI dataset includes more than 10,000 image patches extracted from 400 whole slide images with biopsy-proven results.
DOI: 10.57702/uzm34iv6
title: Neural Collaborative Filtering

description: The dataset is used for neural collaborative filtering, which is a type of collaborative filtering that uses neural networks to learn the relationships between users and items.
DOI: 10.57702/d2qtrqyp
title: Binary Image Classification Dataset

description: The dataset used in the paper is a binary image classification dataset, containing images of size 2x2, 4x4, and 8x8.
DOI: 10.57702/f3oykgmi
title: FGNET

description: Face aging dataset for evaluating age progression methods.
DOI: 10.57702/1ye3f8ef
title: Just Dance dataset

description: A new dance dataset containing synchronized dance motion, music, and lyrics, providing a more comprehensive resource for research in this area.
DOI: 10.57702/9ru3zuhs
title: IBRL dataset

description: A dataset for anomaly detection in wireless sensor networks, generated by injecting faults into normal data.
DOI: 10.57702/6gqiwycj
title: PartImageNet

description: The PartImageNet dataset is a large-scale vehicle part segmentation dataset, containing 24,000 images of 158 classes.
DOI: 10.57702/8mldzide
title: ACDC MRI Cardiac Segmentation Dataset

description: Medical image segmentation dataset
DOI: 10.57702/syjq532g
title: Synapse CT Abdomen Segmentation Dataset

description: Medical image segmentation dataset
DOI: 10.57702/61nuqdi1
title: Counting dataset

description: The dataset used in the paper is a counting probe dataset, which consists of images and corresponding questions or statements about the number of entities in the image.
DOI: 10.57702/zqariweh
title: Visual7W dataset

description: The Visual7W dataset is a visual question answering dataset, which consists of images and corresponding questions.
DOI: 10.57702/gyjvmy07
title: Uniter dataset

description: The Uniter dataset is a multimodal learning dataset, which consists of images and corresponding text.
DOI: 10.57702/f8nx4xl2
title: IXI and FastMRI+ datasets

description: IXI dataset for brain MRI, FastMRI+ dataset for brain MRI
DOI: 10.57702/brdm14rp
title: ATLAS v2.0 dataset

description: ATLAS v2.0 dataset for brain MRI
DOI: 10.57702/dxyfodmi
title: CIFAR-100 and SVHN

description: The dataset used in the paper is CIFAR-100 and SVHN, which are image classification datasets.
DOI: 10.57702/ichnt8su
title: GANs for Mobile Edge Networks

description: The dataset used in the paper is a Generative Adversarial Networks (GANs) dataset, which includes various GANs architectures and their applications in mobile edge networks.
DOI: 10.57702/ds14dthj
title: GDMs for Mobile Edge Networks

description: The dataset used in the paper is a Generative Diffusion Models (GDMs) dataset, which includes various GDMs architectures and their applications in mobile edge networks.
DOI: 10.57702/3li2p8qv
title: VAEs for Mobile Edge Networks

description: The dataset used in the paper is a Variational Autoencoders (VAEs) dataset, which includes various VAEs architectures and their applications in mobile edge networks.
DOI: 10.57702/y54po5fl
title: Generative AI for Semantic Communications

description: The dataset used in the paper is a Generative AI for Semantic Communications dataset, which includes various GAI technologies and their applications in semantic communication.
DOI: 10.57702/nhm8eug1
title: Wikipedia-90M

description: The dataset is used for parsing and is a 90 million word subset of Wikipedia.
DOI: 10.57702/89ahsu4z
title: Fair Adversarial Instance Re-weighting (FAIR)

description: The proposed framework was tested on four datasets, three of which are commonly used benchmarks. Two datasets (German credit and Adult income) come from the UCI ML repository [44]. To our knowledge the Hospital readmission dataset was used in this paper for the ﬁrst time in the context of fairness.
DOI: 10.57702/dicrn173
title: German Credit Dataset

description: The German credit dataset is a dataset used for classification tasks, and it contains sensitive attributes such as credit history.
DOI: 10.57702/4gpwfh6d
title: Tipping Points of Evolving Epidemiological Networks

description: The dataset is used to study the tipping point dynamics of a complex SIS epidemiological adaptive network.
DOI: 10.57702/ahizcuzr
title: Synthetic example

description: The dataset is not explicitly described in the paper, but it is mentioned that the authors used a synthetic example to demonstrate the issue with the envelop theorem.
DOI: 10.57702/2b29u4ah
title: BIWI: A 3D Audio-Visual Corpus of Affective Communication

description: A 3D audio-visual corpus of affective communication.
DOI: 10.57702/hee1euah
title: Concept Sliders Dataset

description: The dataset used for training the Concept Sliders, consisting of paired image data and text prompts.
DOI: 10.57702/u3ciqh64
title: Concept Sliders Test Dataset

description: The dataset used for testing the Concept Sliders, consisting of paired image data and text prompts.
DOI: 10.57702/20mmfr42
title: AeroRIT

description: AeroRIT is a large-scale aerial hyperspectral scene with pixel-wise annotations, composed of challenging factors like shadows, glint, and mixed pixels.
DOI: 10.57702/onoyqv7i
title: Confluence: A Robust Non-IOU Alternative to Non-Maxima Suppression in Object Detection

description: Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection.
DOI: 10.57702/q0toagye
title: MS COCO mini-val dataset

description: The MS COCO mini-val dataset contains 5000 images of 80 classes.
DOI: 10.57702/efzxpi0j
title: SELF-LABELLING VIA SIMULTANEOUS CLUSTERING AND REPRESENTATION LEARNING

description: Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.
DOI: 10.57702/8nb7j1vw
title: COCO 2017 object detection dataset

description: The COCO 2017 object detection dataset is used in this paper for evaluation of the proposed DEYOv2 model.
DOI: 10.57702/g0qrj29a
title: ImageNet-1k dataset

description: The ImageNet-1k dataset is used as the backbone network for the proposed DEYOv2 model.
DOI: 10.57702/haazteyw
title: Clothing Dataset

description: The Clothing dataset contains metadata, text descriptions, and images of the clothing items, with the review score as the label.
DOI: 10.57702/ektrjcoj
title: Chexpert

description: The Chexpert dataset is a large chest radiograph dataset with uncertainty labels and expert comparison.
DOI: 10.57702/g07b6foa
title: IMAGE DATA

description: The MNIST, Fashion-MNIST, CIFAR-10, and CelebA datasets are used for image data.
DOI: 10.57702/jpkg7iir
title: NuclSeg

description: The dataset is used for generalized nuclear segmentation for computational pathology.