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 171–180 of 308 papers

TitleStatusHype
On Deep Learning for computing the Dynamic Initial Margin and Margin Value Adjustment—0
On Deep Learning for Radio Resource Management in A Non-stationary Radio Environment—0
One-Shot Federated Learning with Classifier-Free Diffusion Models—0
On the Elements of Datasets for Cyber Physical Systems Security—0
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models—0
Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels—0
Pseudo Dataset Generation for Out-of-Domain Multi-Camera View Recommendation—0
Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets—0
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture—0
RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution—0
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