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 [1].
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 |