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..
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules
Authors: Kazuki Irie, Jürgen Schmidhuber
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train our FPAs in the generative adversarial networks framework, and evaluate on various image datasets. We evaluate our model on six standard image generation datasets (Celeb A, LSUN-Church, Metfaces, AFHQ-Cat/Dog/Wild; all at the resolution of 64x64), and report both qualitative image quality as well as the commonly used Fr echet Inception Distance (FID) evaluation metric (Heusel et al., 2017). |
| Researcher Affiliation | Academia | Kazuki Irie1 J urgen Schmidhuber1,2 1The Swiss AI Lab, IDSIA, USI & SUPSI, Lugano, Switzerland 2AI Initiative, KAUST, Thuwal, Saudi Arabia |
| Pseudocode | No | The paper uses mathematical equations and describes sequences of operations but does not provide any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | Yes | Our code is public.1 1https://github.com/IDSIA/fpainter |
| Open Datasets | Yes | We use six standard benchmark datasets for image generation: Celeb A (Liu et al., 2015), LSUN Church (Yu et al., 2015), Animal Faces HQ (AFHQ) Cat/Dog/Wild (Choi et al., 2020), and Met Faces (Karras et al., 2020a). |
| Dataset Splits | No | The paper computes FID scores every 5K training steps to monitor performance but does not specify explicit train/validation/test dataset splits by percentage or sample counts. |
| Hardware Specification | Yes | Any training run can be completed within one to three days on a single V100 GPU. |
| Software Dependencies | No | The paper mentions the use of specific implementations (e.g., "unofficial public Light GAN implementation", "official implementation of Style GAN3", "pytorch-fid implementation", "denoising-diffusion-pytorch"), but it does not specify version numbers for these software components or other general dependencies like Python or PyTorch. |
| Experiment Setup | Yes | The batch size and learning rate are fixed to 20 and 2e 4 respectively. We provide all hyper-parameters in Appendix B.2 and discuss training/generation speed in Appendix C.2. Table 3 summarises the corresponding results. |