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..
On Efficient Transformer-Based Image Pre-training for Low-Level Vision
Authors: Wenbo Li, Xin Lu, Shengju Qian, Jiangbo Lu
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To comprehensively diagnose the in๏ฌuence of pre-training, we design a whole set of principled evaluation tools that uncover its effects on internal representations. and Based on the study, we successfully develop state-of-theart models for multiple low-level tasks. |
| Researcher Affiliation | Collaboration | Wenbo Li1 , Xin Lu2* , Shengju Qian1 and Jiangbo Lu3 1The Chinese University of Hong Kong 2Deeproute.ai 3Smart More Corporation |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Following [Chen et al., 2021], we adopt the Image Net [Deng et al., 2009] dataset in the pre-training stage. |
| Dataset Splits | No | The paper mentions using a 'test dataset' for CKA computation and 'fine-tuning is performed on a single task', but does not explicitly provide details about training, validation, or test dataset splits in the main text. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments with specific models or types. |
| Software Dependencies | No | The paper does not provide specific version numbers for key software components or libraries used in the experiments. |
| Experiment Setup | Yes | We uniformly set the block number in each transformer stage to 6, the expansion ratio of the feed-forward network (FFN) to 2 and the window size to (6, 24). and training patch size is 64x64 (ours is 48x48). (from Table 3 footnote). |