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..
Deep Interleaved Network for Single Image Super-Resolution with Asymmetric Co-Attention
Authors: Feng Li, Runmin Cong, Huihui Bai, Yifan He
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | img_092 from Urban100, 8023 from BSDS100, HR Bicubic SRCNN Lap SRN DRRN, Mem Net EDSR SRFBN RDN |
| Researcher Affiliation | Academia | No author affiliations, university names, company names, or email domains are provided in the given text snippets to determine the affiliation type. |
| Pseudocode | No | The provided text does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The provided text does not contain any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | img_092 from Urban100, 8023 from BSDS100 |
| Dataset Splits | No | The provided text mentions datasets like Urban100 and BSDS100 but does not specify any dataset split information such as exact percentages, sample counts, or a detailed splitting methodology. |
| Hardware Specification | No | The provided text does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running experiments. |
| Software Dependencies | No | The provided text does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The provided text does not contain specific experimental setup details such as concrete hyperparameter values or training configurations. |