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

Showing 251260 of 308 papers

TitleStatusHype
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling AutonomyCode0
Location-Aware Visual Question Generation with Lightweight ModelsCode0
LoFT: LoRA-fused Training Dataset Generation with Few-shot GuidanceCode0
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic DataCode0
TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language ModelsCode0
Masked Face Dataset Generation and Masked Face RecognitionCode0
Semantically Rich Local Dataset Generation for Explainable AI in GenomicsCode0
Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time CapabilityCode0
SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented DataCode0
Sim-MEES: Modular End-Effector System Grasping Dataset for Mobile Manipulators in Cluttered EnvironmentsCode0
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