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..
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Authors: Qi Zhang, Yifei Wang, Yisen Wang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first present the main empirical results of our proposed U-MAE loss on different real-world datasets with different backbones. Then we conduct a series of experiments to understand how well the U-MAE loss works. |
| Researcher Affiliation | Academia | 1 Key Lab. of Machine Perception (Mo E), School of Intelligence Science and Technology, Peking University 2 School of Mathematical Sciences, Peking University 3 Institute for Artificial Intelligence, Peking University |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Code is available at https://github.com/zhangq327/U-MAE. |
| Open Datasets | Yes | extensive experiments are conducted on CIFAR-10 [21], Image Net-100 [8], and Image Net-1K [8]. |
| Dataset Splits | No | No explicit details on train/validation/test splits (e.g., percentages, sample counts, or citations to predefined splits) were found in the main text. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) were found. |
| Software Dependencies | No | No specific ancillary software details, such as library or solver names with version numbers, were found in the paper. |
| Experiment Setup | Yes | The mask ratio is set to 0.75. For U-MAE, the coefficient of the uniformity term is set to 0.01. On CIFAR-10, we pretrain the model for 2000 epochs with batch size 4096 and weight decay 0.05. On Image Net-100 and Image Net-1K, we pretrain the model for 200 epochs with batch size 1024 and weight decay 0.05. |