Diffusion Model for Dense Matching
Authors: Jisu Nam, Gyuseong Lee, Sunwoo Kim, Hyeonsu Kim, Hyoungwon Cho, Seyeon Kim, Seungryong Kim
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate significant performance improvements of our method over existing approaches, and the ablation studies validate our design choices along with the effectiveness of each component. |
| Researcher Affiliation | Collaboration | 1Korea University 2LG Electronics 3KT |
| Pseudocode | No | The paper describes the model and processes using mathematical equations and textual descriptions, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code and pretrained weights are available at https://ku-cvlab.github.io/Diff Match. |
| Open Datasets | Yes | We conducted comprehensive experiments in geometric matching for four datasets: HPatches (Balntas et al., 2017), ETH3D (Schops et al., 2017), Image Net-C (Hendrycks & Dietterich, 2019) corrupted HPatches and Image Net-C corrupted ETH3D. |
| Dataset Splits | No | The paper mentions training on DPED-City Scape-ADE and COCO-augmented DPED-City Scape-ADE, and evaluating on HPatches and ETH3D, but it does not specify explicit training, validation, and test splits (e.g., percentages or sample counts) for these datasets. |
| Hardware Specification | Yes | All our experiments were conducted on 6 24GB RTX 3090 GPUs. |
| Software Dependencies | No | The paper states, 'We implemented the network in Py Torch (Paszke et al., 2019) and used the Adam W optimizer (Loshchilov & Hutter, 2017),' but it does not specify the version numbers for PyTorch or other libraries used for implementation. |
| Experiment Setup | Yes | For the denoising diffusion model, we train 121M modified U-Net based on (Nichol & Dhariwal, 2021) with the learning rate to 1 10 4 and trained the model for 130,000 iterations with a batch size of 24. For the flow upsampling diffusion model, we used a learning rate of 3 10 5 and finetuned the pretrained conditional denoising diffusion model for 20,000 iterations with a batch size of 2. |