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..
Basic Binary Convolution Unit for Binarized Image Restoration Network
Authors: Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, Luc Van Gool
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on different IR tasks, and our BBCU significantly outperforms other BNNs and lightweight models, which shows that BBCU can serve as a basic unit for binarized IR networks. |
| Researcher Affiliation | Collaboration | Bin Xia1, Yulun Zhang2, Yitong Wang3, Yapeng Tian4, Wenming Yang1 , Radu Timofte5, and Luc Van Gool2 1Tsinghua University 2ETH Z urich 3Byte Dance Inc 4University of Texas at Dallas 5University of W urzburg |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Zj-Bin Xia/BBCU |
| Open Datasets | Yes | We train all models on DIV2K (Agustsson & Timofte, 2017), which contains 800 high-quality images. Besides, we adopt widely used test sets for evaluation and report PSNR and SSIM. [...] In addition, we use Set5 (Bevilacqua et al., 2012), Set14 (Zeyde et al., 2010), B100 (Martin et al., 2001), Urban100 (Huang et al., 2015), and Manga109 (Matsui et al., 2017) for evaluation. |
| Dataset Splits | No | The paper specifies training parameters and datasets used for training and testing, but it does not explicitly describe a validation data split (e.g., 'X% for validation', or a specific validation set name) separate from training and test. |
| Hardware Specification | Yes | We implement our models with a Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Matlab standard JPEG encoder' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The mini-batch contains 16 images with the size of 192 192 randomly cropped from training data. We set the initial learning rate to 1 10 4, train models with 300 epochs, and perform halving every 200 epochs. [...] The amplification factor k in the residual alignment is set to 130. |