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..
Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues
Authors: Jiaxuan Chen, Yu Qi, Gang Pan
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments were carried out with a benchmark dataset in comparison with existing approaches. |
| Researcher Affiliation | Academia | 1State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China. 2College of Computer Science and Technology, Zhejiang University, Hangzhou, China. 3MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China. |
| Pseudocode | No | The paper describes the proposed framework and its components using text and equations (e.g., Eq. 1-13) but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We experimented with a popular publicly available f MRI dataset, which is called Generic Object Decoding (GOD) dataset (Horikawa & Kamitani, 2017). |
| Dataset Splits | No | The paper states: 'For each subject, training set consists 1200 f MRI-image pairs, and the testing made up of 50 f MRI recordings with corresponding images.' It does not explicitly define a separate validation dataset split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Adam solver (Kingma & Ba, 2014)' but does not specify version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | The parameter setting of VQ-f MRI for all experiments is summarized as follows. Enocders of VQ-VAE: 2 convolutional layers (stride 2, kernel 4 4, and padding 1), followed by two residual blocks; Deocders of VQ-VAE: two residual blocks, followed by 3 transposed convolutions (stride 2, kernel 4 4, and padding 1); Codebooks: ZL R8 32 (image y R64 64 3), and Z R8 128 (image y R128 128 3). We implemented the image classi๏ฌer, inpainting, and SR modules using the UNet with 2 downsampling and 2 upsampling layers (stride 2, kernel 4 4, and padding 1). Adam solver (Kingma & Ba, 2014) is employed to optimize the parameters with a learning rate of 2e-4. |