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..
A Plug-and-Play Image Registration Network
Authors: Junhao Hu, Weijie Gan, Zhixin Sun, Hongyu An, Ulugbek Kamilov
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical results on OASIS and CANDI datasets show that our methods achieve state-of-the-art performance on DIR. |
| Researcher Affiliation | Academia | Junhao Hu , Weijie Gan , Zhixin Sun, Hongyu An, Ulugbek S. Kamilov Washington University in St. Louis, St. Louis, MO, USA EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm block was found. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We validated PIRATE and PIRATE+ on two widely used datasets: OASIS-1 (Marcus et al., 2007) and CANDI (Kennedy et al., 2012). |
| Dataset Splits | No | For both datasets, we randomly shuffled the images and allocated 100 unique image pairs for training and another 100 unique image pairs for evaluation. No explicit mention of a separate validation split or its size was found. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'Dn CNN' but does not provide specific version numbers for any software, libraries, or programming languages used. |
| Experiment Setup | Yes | In training phase of the denoiser, we used Adam (Kingma & Ba, 2014) optimizer with learning rate 1e 4 for 400 epochs... For DEQ in PIRATE+, we used Adam optimizer with learning rate 1 10 5 for 50 epochs. We assigned w0 to 1, w1 to 5, w2 to 1 for both datasets... PIRATE achieved the best performance by assigning γ0 to 5 105, α to 5 10 1, and τ to 1 10 7 for OASIS-1. α was assigned to 5 10 1 for CANDI while γ0 and α stayed the same. |