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 [1].
Learning Spectral Dictionary for Local Representation of Mesh
Authors: Zhongpai Gao, Junchi Yan, Guangtao Zhai, Xiaokang Yang
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate that our model produces state-of-the-art results with a much smaller model size. |
| Researcher Affiliation | Academia | Zhongpai Gao , Junchi Yan , Guangtao Zhai and Xiaokang Yang Mo E Key Lab of Arti๏ฌcial Intelligence, AI Institute, Shanghai Jiao Tong University EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code has been formally released at: https://github.com/Gaozhongpai/PaiConvMesh. |
| Open Datasets | Yes | In line with [Gao et al., 2021], we evaluate our model on two datasets: COMA [Ranjan et al., 2018] and DFAUST [Bogo et al., 2017]. |
| Dataset Splits | Yes | Both two datasets are split into training and test set with a ratio of 9:1 and randomly select 100 samples from the training set for validation. |
| Hardware Specification | Yes | We compare different convolutional operations that are implemented in Py Torch on the same machine with an AMD 3700X @3.6GHz CPU and an NVIDIA RTX2080Ti GPU. |
| Software Dependencies | No | The paper mentions that the operations are 'implemented in Py Torch'. However, it does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use Adam [Kingma and Ba, 2014] optimizer with a learning rate of 0.001 and reduce the learning rate with a decay rate of 0.99 in every epoch. The batch size is 32 and the total epoch number is 300. Weight decay regularization is used for the network parameters except for the spectral dictionary of weighting matrices. |