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..
Corrupted Image Modeling for Self-Supervised Visual Pre-Training
Authors: Yuxin Fang, Li Dong, Hangbo Bao, Xinggang Wang, Furu Wei
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our approach achieves compelling results in vision benchmarks, such as Image Net classification and ADE20K semantic segmentation. |
| Researcher Affiliation | Collaboration | Yuxin Fang 1, 2 Li Dong 2 Hangbo Bao 2 Xinggang Wang 1 Furu Wei 2 1 School of EIC, Huazhong University of Science & Technology 2 Microsoft Research |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It mentions using 'publicly available3 pre-trained DALL-E d VAE weight' and implements 'the pre-training using the codebase of BEi T', but does not provide a link or statement for their own implementation. |
| Open Datasets | Yes | Image Net-1K (Deng et al., 2009) training data is used to pre-train the small BEi T and the enhancer. |
| Dataset Splits | Yes | We study of the Vi T-B model s 100-epoch fine-tuning performance on Image Net-1k val set with different pre-training schedules in Table 11. |
| Hardware Specification | Yes | We conduct experiments on 16 or 32 V100 GPUs with 32GB memory. |
| Software Dependencies | No | The paper mentions software components like 'Adam W optimizer' and 'Mixed precision and deepspeed acceleration' but does not provide specific version numbers for these or other key software dependencies like PyTorch or Python. |
| Experiment Setup | Yes | A.4 PRE-TRAINING & FINE-TUNING CONFIGURATIONS. This section provides detailed settings such as 'Optimizer Adam W', 'Pre-training Epochs 300', 'Peak Learning Rate 1.5e-3', 'Batch Size 2048', and 'Weight Decay 0.05'. |