SOTAVerified

Dataset Generation

The task involves enhancing the training of target application (e.g. autonomous driving systems) by generating datasets of diverse and critical elements (e.g. traffic scenarios). Traditional methods rely on expensive and limited datasets, which often fail to capture rare but essential situations that can pose risks during testing.

Papers

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Showing 226–250 of 308 papers

TitleStatusHype
ProGen: Progressive Zero-shot Dataset Generation via In-context FeedbackCode1
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel LogisticsCode1
Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing—0
SDW-ASL: A Dynamic System to Generate Large Scale Dataset for Continuous American Sign Language—0
Synthetic Dataset Generation for Privacy-Preserving Machine Learning—0
A Framework for Large Scale Synthetic Graph Dataset Generation—0
Transfer learning for self-supervised, blind-spot seismic denoising—0
Comparison of synthetic dataset generation methods for medical intervention rooms using medical clothing detection as an example—0
Conversational QA Dataset Generation with Answer Revision—0
On the Elements of Datasets for Cyber Physical Systems Security—0
Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time CapabilityCode0
RealFlow: EM-based Realistic Optical Flow Dataset Generation from VideosCode1
Synthetic Dataset Generation for Adversarial Machine Learning ResearchCode6
Automatic dataset generation for specific object detection—0
HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D ReconstructionCode1
A universal synthetic dataset for machine learning on spectroscopic dataCode0
Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset GenerationCode0
Learning to Answer Visual Questions from Web VideosCode1
Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard—0
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting—0
Closing the Loop: A Framework for Trustworthy Machine Learning in Power SystemsCode0
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender SystemsCode0
ZeroGen: Efficient Zero-shot Learning via Dataset GenerationCode1
Feasible Low-thrust Trajectory Identification via a Deep Neural Network Classifier—0
ADG-Pose: Automated Dataset Generation for Real-World Human Pose EstimationCode0
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