CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
Authors: Shichong Peng, Seyed Alireza Moazenipourasil, Ke Li
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show CHIMLE significantly outperforms the prior best IMLE, GAN and diffusion-based methods in terms of image fidelity and mode coverage across four tasks, namely night-to-day, 16 single image super-resolution, image colourization and image decompression. Quantitatively, our method improves Fréchet Inception Distance (FID) by 36.9% on average compared to the prior best IMLE-based method, and by 27.5% on average compared to the best non-IMLE-based general-purpose methods. |
| Researcher Affiliation | Collaboration | Shichong Peng1, Alireza Moazeni1, Ke Li1,2 1APEX Lab School of Computing Science 2Google Simon Fraser University {shichong_peng,seyed_alireza_moazenipourasil,keli}@sfu.ca |
| Pseudocode | Yes | Algorithm 1 Conditional Hierarchical IMLE Algorithm for a Single Data Point |
| Open Source Code | Yes | More results and code are available on the project website at https://niopeng.github.io/CHIMLE/. |
| Open Datasets | Yes | Details for datasets are included in the appendix. |
| Dataset Splits | No | No. While the paper states 'Details for datasets are included in the appendix' and the checklist claims 'data splits' are specified, the main body of the paper does not explicitly detail the training/validation/test dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | We trained all models for 150 epochs with a mini-batch size of 1 using the Adam optimizer [44] on an NVIDIA V100 GPU. |
| Software Dependencies | No | No. The paper mentions using the 'Adam optimizer [44]' and various metrics and baselines with citations, but does not specify software dependencies with version numbers (e.g., Python, PyTorch, or library versions). |
| Experiment Setup | Yes | We trained all models for 150 epochs with a mini-batch size of 1 using the Adam optimizer [44] on an NVIDIA V100 GPU. |