Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules
Authors: Kazuki Irie, Jürgen Schmidhuber
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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. |