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..
Task-Specific Scene Structure Representations
Authors: Jisu Shin, Seunghyun Shin, Hae-Gon Jeon
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a variety of experiments on low-level vision tasks, including self-supervised joint depth upsampling (Sec.4.1) and unsupervised single image denoising (Sec.4.2), to demonstrate the effectiveness of our SSGNet. |
| Researcher Affiliation | Academia | Jisu Shin*, Seunghyun Shin*and Hae-Gon Jeon AI Graduate School, GIST, South Korea EMAIL, EMAIL |
| Pseudocode | No | The paper describes the network architecture (Fig. 2) and loss functions, but it does not provide any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source codes are available at https://github.com/jsshin98/SSGNet. |
| Open Datasets | Yes | Prior to the evaluations, we train our SSGNet on a well-known NYUv2 dataset (Silberman and Fergus 2011), consisting of 1,000 training images and 449 test images. |
| Dataset Splits | No | The paper mentions training and test sets for NYUv2 but does not specify a separate validation set or describe how validation was performed for any dataset used. |
| Hardware Specification | Yes | The training on SSGNet took about 40 hours on two NVIDIA Tesla v100 GPUs. |
| Software Dependencies | No | The paper mentions 'public Pytorch' but does not specify a version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | The learning rate and the batch size are set to 0.0001 and 4 on SSGNet, respectively. We train the proposed framework on images with a 256 256 resolution. ... the hyperparameter γ is set to 0.9 in our implementation. where λ is the hyper-parameter, and is empirically set to 40. |