Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models
Authors: Sharon Zhou, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, Li F. Fei-Fei, Michael Bernstein
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test HYPE across six state-of-the-art generative adversarial networks and two sampling techniques on conditional and unconditional image generation using four datasets: Celeb A, FFHQ, CIFAR-10, and Image Net. We ๏ฌnd that HYPE can track the relative improvements between models, and we con๏ฌrm via bootstrap sampling that these measurements are consistent and replicable. |
| Researcher Affiliation | Academia | Sharon Zhou , Mitchell L. Gordon , Ranjay Krishna , Austin Narcomey , Li Fei-Fei , Michael S. Bernstein Stanford University EMAIL |
| Pseudocode | No | The paper describes methods in text and uses diagrams, but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | We deploy HYPE at https://hype.stanford.edu, where researchers can upload a model and retrieve a HYPE score. This describes a deployed service, not necessarily open-source code for the methodology. |
| Open Datasets | Yes | Datasets. We evaluate on four datasets. (1) Celeb A-64 [37]... (2) FFHQ-1024 [26]... (3) CIFAR-10 [31]... (4) Image Net-5... from the Image Net dataset [13] |
| Dataset Splits | No | The paper describes the datasets used to train the GANs and the sampling strategy for the human evaluation (50 real and 50 fake images), but does not specify explicit train/validation/test splits for these datasets for model training or for the evaluation itself. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | Image exposures are in the range [100ms, 1000ms], derived from the perception literature [17]. All blocks begin at 500ms and last for 150 images (50% generated, 50% real)... Exposure times are raised at 10ms increments and reduced at 30ms decrements, following the 3-up/1-down adaptive staircase approach... |