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..
Low Distortion Block-Resampling with Spatially Stochastic Networks
Authors: Sarah Hong, Martin Arjovsky, Darryl Barnhart, Ian Thompson
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In section 4 we perform both qualitative and qualitative experiments showing the workings and excellent performance of SSNs. We experiment with the FFHQ [18] faces and the LSUN churches [39] datasets at a resolution of 256 256 pixels. We provide both quantitative and qualitative experiments. |
| Researcher Affiliation | Collaboration | Sarah Jane Hong Latent Space EMAIL Martin Arjovsky École Normale Supérieure EMAIL Darryl Barnhart Latent Space EMAIL Ian Thompson Latent Space EMAIL |
| Pseudocode | No | The paper describes the architecture and algorithm through text and diagrams (Figure 1, Figure 3), but does not include any formal pseudocode blocks or algorithms. |
| Open Source Code | No | The paper cites external GitHub repositories for pretrained StyleGAN2 models ([1], [2]) that were used in the work, but does not provide a link or explicit statement for the release of its own methodology's source code. |
| Open Datasets | Yes | We experiment with the FFHQ [18] faces and the LSUN churches [39] datasets at a resolution of 256 256 pixels. |
| Dataset Splits | No | The paper mentions using datasets for experiments but does not provide specific details on train/validation/test splits, percentages, or the methodology used for data partitioning. |
| Hardware Specification | Yes | training a single Style GAN2 model from scratch on LSUN churches takes 781 GPU hours on V100s |
| Software Dependencies | No | The paper mentions using StyleGAN2 and SPADE-like architectures, but does not specify version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or specific dependencies used in their implementation. |
| Experiment Setup | Yes | The latent code has dimension z R4 4 512 for SSN and z R1 1 512) as per Style GAN2 s default configuration. We also utilize skip connections, the general architecture, and the R1 and path length regularization (with weights of 1 and 2 respectively) of [18]. We chose the hyperparameter of λD = 100 for our qualitative experiments |