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..
Critical windows: non-asymptotic theory for feature emergence in diffusion models
Authors: Marvin Li, Sitan Chen
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our bounds with experiments on synthetic data and show that critical windows may serve as a useful tool for diagnosing fairness and privacy violations in real-world diffusion models. |
| Researcher Affiliation | Academia | 1Harvard College, Cambridge, MA, USA 2John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. |
| Pseudocode | No | The paper describes mathematical frameworks and proofs but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for its methodology, nor does it provide a direct link to a code repository for this work. |
| Open Datasets | Yes | We applied our attack to a DDPM that was trained on CIFAR-10 in (Duan et al., 2023) |
| Dataset Splits | No | For the Membership Inference Attack, the paper mentions '1000 training data samples and 1000 CIFAR-10 held-out samples' but does not specify a separate validation split or explicit training/validation/test splits for any of its experiments. |
| Hardware Specification | No | The paper mentions models like Stable Diffusion v2.1 but does not specify the hardware (e.g., GPU, CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'CLIP with the ViT-B/32 Transformer architecture' and refers to the 'DDPM scheduler', but it does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We produced 250 images from SD2.1., using 500 time steps from the DDPM scheduler (Ho et al., 2020a) and the prompt Color splash wide photo of a car in the middle of empty street, detailed, highly realistic, brightly colored car, black and white background. |