datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
generative-ai-red-teaming
About this dataset
This dataset is an unofficial transformed clone of the Generative AI Red-Teaming
(GRT) dataset created by Humane
Intelligence (HI). This dataset collates
findings from the Generative AI Red-Teaming Challenge conducted at AI Village
within DEFCON 31. It is provided as part of HI's inaugural algorithmic bias
bounty.
The original lives on
GitHub at:
humane-intelligence/bias-bounty-data
Differences
This version of the GRT dataset differs from the original… See the full description on the dataset page: https://huggingface.co/datasets/jinnovation/generative-ai-red-teaming.generative-world-renderer-clips
A Scaling Recipe for Generative World Renderer
NeurIPS 2026 Evaluations & Datasets Track — anonymous submission.
Reviewer Sample — NeurIPS 2026 Submission.
This repository currently hosts a 40-clip flattened reviewer sample (≈ 2.8 GB) so that NeurIPS 2026 reviewers can inspect data quality and format without per-request gated access. The full dataset described in the accompanying paper — approximately 4 M frames, 40 hours of playtime, 720p / 30 FPS, ~11 k sub-clips, with five… See the full description on the dataset page: https://huggingface.co/datasets/anonymous111111/generative-world-renderer-clips.dynamicPDBriemannian-generative-decoder
Riemannian generative decoder dataset
This repository contains the data related to the paper "Riemannian generative decoder".
Project Page: https://yhsure.github.io/riemannian-generative-decoder
Code Repository: https://github.com/yhsure/riemannian-generative-decoder
Abstract
Riemannian representation learning typically relies on an encoder to estimate densities on chosen manifolds. This involves optimizing numerically brittle objectives, potentially harming model… See the full description on the dataset page: https://huggingface.co/datasets/yhsure/riemannian-generative-decoder.Generative-VQA-V2-Curated
Generative-VQA-V2-Curated
A curated, balanced, and cleaned version of the VQA v2 dataset specifically optimized for Generative Visual Question Answering.
This dataset transforms the standard VQA task into a generative challenge by removing "yes/no" shortcuts and balancing answer distributions to prevent model over-fitting on dominant classes.
Dataset Summary
The primary goal of this curated set is to provide a "clean" signal for training multimodal models by:
Eliminating… See the full description on the dataset page: https://huggingface.co/datasets/Deva8/Generative-VQA-V2-Curated.generative-ai-reviews-50kXOCEAN_generativegenerative-manim-human-feedbackgenerative_Aibpmn-model-ia-generativehw_1_generative_models