Denoising Normalizing Flow
Authors: Christian Horvat, Jean-Pascal Pfister
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 christian.horvat@unibe.ch Jean-Pascal Pfister Department of Physiology University of Bern Bern, Switzerland jeanpascal.pfister@unibe.ch |
| 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. |