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..
Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders
Authors: Youngwan Lee, Jeffrey Ryan Willette, Jonghee Kim, Juho Lee, Sung Ju Hwang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments follow the same architecture, settings, and pre-training recipe as MAE (He et al., 2022), and we find that the simple addition of a teacher (RC-MAE) consistently outperforms MAE in all model sizes (e.g., Vi T-S, Vi T-B, and Vi T-L) when fine-tuned for Image Net classification. |
| Researcher Affiliation | Collaboration | 1Electronics and Telecommunications Research Institute (ETRI), South Korea 2Korea Advanced Institute of Science and Technology (KAIST), South Korea |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper mentions and links to the official code for MAE (He et al., 2022), a baseline method, but does not provide an explicit statement or link for the open-source code of RC-MAE, the methodology described in this paper. |
| Open Datasets | Yes | For experiments, we pre-train on Image Net-1K and evaluate linear probing (LN) and end-to-end finetuning (FT) for classification and COCO object detection & instance segmentation for which we use the Mask R-CNN benchmark (Li et al., 2021) for dense prediction. |
| Dataset Splits | Yes | For experiments, we pre-train on Image Net-1K and evaluate linear probing (LN) and end-to-end finetuning (FT) for classification and COCO object detection & instance segmentation... and ...we use the same model weights (RC-MAE w/Vi TB for 1600epoch) fine-tuned on the original Image Net-1K as shown in Table 4 and only test without any specialized fine-tuning on the different validation sets, such as Image Net-C (Hendrycks & Dietterich, 2019),-A (Hendrycks et al., 2021b),-R (Hendrycks et al., 2021a), and -Sketch (Wang et al., 2019). |
| Hardware Specification | Yes | Although He et al. (2022) used 128 TPU-v3 cores, we have tried to reproduce the baseline MAE and train our RC-MAE on the same local GPU environment, which has 8 NVIDIA V100 GPUs (32GB) for more accessibility in the community. |
| Software Dependencies | No | The paper mentions software components like Pytorch, AdamW, and LARS optimizer, but does not provide specific version numbers for these or other key libraries/frameworks, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | The paper provides detailed settings for pre-training (Table 9), end-to-end fine-tuning (Table 10), and linear probing (Table 11), including optimizer, learning rates, batch sizes, epochs, weight decay, and various augmentation strategies. |