Regularizing Neural Networks with Meta-Learning Generative Models
Authors: Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |