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..
Denoising Normalizing Flow
Authors: Christian Horvat, Jean-Pascal Pfister
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate on naturalistic data that our method learns meaningful latent representations without sacrificing the sample quality. |
| Researcher Affiliation | Academia | Christian Horvat Department of Physiology University of Bern Bern, Switzerland EMAIL Jean-Pascal Pfister Department of Physiology University of Bern Bern, Switzerland EMAIL |
| Pseudocode | Yes | DNF Algorithm: Training of Denoising Normalizing Flow for qσ( x|x) = N( x; x, σ2ID). |
| Open Source Code | Yes | 4Our main code is available at https://github.com/chrvt/denoising-normalizing-flow and is based on the original M flow implementation made public by the authors of [10] under the MIT license. |
| Open Datasets | Yes | Therefore, [10] uses a Style GAN2 model [23] trained on the FFHQ dataset [22] to generate an d dimensional manifold by only varying the first d latent variables while keeping the remaining fixed. |
| Dataset Splits | No | The paper mentions training on a number of images, epochs, and batch sizes, but does not provide specific train/validation/test split percentages or counts in the main text. |
| Hardware Specification | No | The paper mentions running experiments on a 'GPU' but does not specify the exact model, manufacturer, or other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions using implementations based on other authors' work and licenses (MIT, Apache License 2.0, GPLv3.0) but does not specify software versions (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | For the DNF, we use Gaussian noise with σ2 = 0.01 and λ = 1. ... For that, we first train an DNF on 104 images using 100 epochs with σ2 = 0.1 and λ = 1000. ... We train the models on 2 104 images for 200 epochs. |