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..
Regularizing Neural Networks with Meta-Learning Generative Models
Authors: Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on six datasets showed that MGR is effective particularly when datasets are smaller and stably outperforms baselines. |
| Researcher Affiliation | Collaboration | Shin ya Yamaguchi (NTT), Daiki Chijiwa (NTT), Sekitoshi Kanai (NTT), Atsutoshi Kumagai (NTT), Hisashi Kashima (Kyoto University) |
| Pseudocode | Yes | Algorithm 1 Meta Generative Regularization |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology described in this paper was found. Links provided (https://github.com/pytorch/vision, https://github.com/NVlabs/stylegan2-ada) are for third-party tools used in the research. |
| Open Datasets | Yes | We used six image datasets for classification tasks in various domains: Cars [12], Aircraft [27], Birds [28], DTD [29], Flowers [30], and Pets [31]. |
| Dataset Splits | Yes | We randomly split a dataset into 9 : 1 and used the former as D and the latter as Dval. |
| Hardware Specification | Yes | We ran the experiments three times on a 24-core Intel Xeon CPU with an NVIDIA A100 GPU with 40GB VRAM |
| Software Dependencies | No | The paper mentions software like PyTorch, Kornia, and UMAP but does not provide specific version numbers for any of them. For example, 'We used the Image Net pre-trained weights of Res Net-18 distributed by Py Torch.' |
| Experiment Setup | Yes | We trained fθ by the Nesterov momentum SGD for 200 epochs with a momentum of 0.9, and an initial learning rate of 0.01; we decayed the learning rate by 0.1 at 60, 120, and 160 epochs. We trained Fϕ by the Adam optimizer for 200 epochs with a learning rate of 1.0 10 4. We used mini-batch sizes of 64 for D and 64 for Dp. |