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..
Large Occluded Human Image Completion via Image-Prior Cooperating
Authors: Hengrun Zhao, Yu Zeng, Huchuan Lu, Lijun Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we demonstrate our method s superior performance compared to state-of-the-art methods. ... Experiments Datasets ... Implementation Details ... Ablation Study |
| Researcher Affiliation | Academia | Hengrun Zhao*1, Yu Zeng*2, Huchuan Lu 1, Lijun Wang1, 1Dalian University of Technology 2Johns Hopkins University |
| Pseudocode | No | The paper describes its methods in text and with architectural diagrams (e.g., Fig. 4, Fig. 6), but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Zhao Hengrun/LOHC. |
| Open Datasets | Yes | As a result, we opted to use the AHP dataset (Zhou et al. 2021) |
| Dataset Splits | Yes | For testing, we used the original dataset s validation set, which amounted to 3400 pieces, while for training, we utilized the original dataset s training set of 53199 images. |
| Hardware Specification | Yes | We use the Pytorch framework for our implementation and train on an Nvidia A100 GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch framework' and 'U-Net' but does not specify their version numbers or any other software dependencies with specific versions. |
| Experiment Setup | Yes | We trained the coarse network with a batch size of 128 and set the learning rate to 1e-4. For the refinement network, we used a smaller batch size of 16 and set the learning rate to 1e-3. The learning rates of all discriminators are set to 1e-6. |