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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
PsyNet: Self-Supervised Approach to Object Localization Using Point Symmetric Transformation
Authors: Kyungjune Baek, Minhyun Lee, Hyunjung Shim10451-10459
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,772 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,302 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Based on extensive evaluations, we observe that the proposed method records new state-of-the-art performance in three ο¬ne-grained datasets for unsupervised object localization. |
| Researcher Affiliation | Academia | Kyungjune Baek, Minhyun Lee, Hyunjung Shim School of Integrated Technology, Yonsei University, Incheon, South Korea EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The source code for this work is available at https://github. com/Fried Ronaldo/Psy Net. |
| Open Datasets | Yes | We conduct experiments on the ο¬ne-grained datasets: CUB200-2011 (Wah et al. 2011), Stanford Cars (Krause et al. 2013) and FGVC-Aircraft (Maji et al. 2013). |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific details on validation dataset splits (e.g., percentages, counts, or explicit validation set usage). |
| Hardware Specification | No | The paper mentions running experiments 'on GPU' but does not provide specific hardware details like GPU model numbers, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | For the ο¬nd-grained object localization task, we initialize the network using an Image Net pre-trained model before training... As the backbone networks, VGG16... and SERes Net50... with the batch normalization... are chosen. The control parameters of PST are xand ytranslation and they are denoted as tx and ty. [...] We use SGD as the optimizer to train this self-supervised model. |