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..
IDPT: Interconnected Dual Pyramid Transformer for Face Super-Resolution
Authors: Jingang Shi, Yusi Wang, Songlin Dong, Xiaopeng Hong, Zitong Yu, Fei Wang, Changxin Wang, Yihong Gong
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and visualizations on various datasets demonstrate the superiority of the proposed method for face super-resolution tasks. 4 Experiments |
| Researcher Affiliation | Academia | Jingang Shi1 , Yusi Wang1 , Songlin Dong1 , Xiaopeng Hong1 , Zitong Yu2 , Fei Wang1 , Changxin Wang1 and Yihong Gong1 1School of Software Engineering, Xi an Jiaotong University 2School of Electrical and Electronic Engineering, Nanyang Technological University |
| Pseudocode | No | The paper describes the model architecture and training process in prose and diagrams but does not provide pseudocode or a formally labeled algorithm block. |
| Open Source Code | No | We also thank Mindspore for the support of this work.2 https://www.mindspore.cn/ (This link is to the Mindspore framework, not the specific code for the paper's methodology, and the text does not state that their code is open-source or available at this link.) |
| Open Datasets | Yes | The Celeb A dataset [Liu et al., 2015] is utilized to train IDPT in the experiments. ... and 50 images from Helen dataset [Le et al., 2012] to conduct the experiments. |
| Dataset Splits | No | The paper describes how the training and testing sets are constructed and selected, but it does not specify explicit percentages or sample counts for training/validation/test splits, nor does it explicitly mention a dedicated validation set split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running experiments. |
| Software Dependencies | No | The experiments are implemented on Mindspore and Pytorch. (No version numbers provided for software dependencies, which is required for reproducibility.) |
| Experiment Setup | Yes | For UPFE, it produces feature maps with channel number of 32 for the first encoder stage, while the parameter α of Leaky ReLU is set to 0.01. In the dual pyramid structure, the maximum number of stages L is set to 4. ... For STMs, the attention head number and window size are set to 2i − 1 (i = 1, ..., L) and 8 × 8, respectively. We train IDPT by AdamW optimizer with β1 = 0.9, β2 = 0.99, ϵ = 10−8, and weight decay is set to 0.02. The learning rate is 2 × 10−4 and the batchsize is set to 16. Loss Setting. For IDPT, we set λAdv = 0 and λPcp = 0. For IDPT-GAN, we empirically set λAdv = 0.005 and λPcp = 0.1. |