Connecting Sphere Manifolds Hierarchically for Regularization
Authors: Damien Scieur, Youngsung Kim
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimented the proposed method using five publicly available datasets, namely CIFAR100 (Krizhevsky, 2009), Caltech-UCSD Birds 200 (CUB200) (Welinder et al., 2010), Stanford-Cars (Cars) (Krause et al., 2013), Stanford-dogs (Dogs) (Khosla et al., 2011), and Tiny-Image Net (Tiny Im Net) (Deng et al., 2009). |
| Researcher Affiliation | Industry | 1Samsung SAIT AI Lab, Montreal 2Samsung Advanced Institute of Technology (SAIT). |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We experimented the proposed method using five publicly available datasets, namely CIFAR100 (Krizhevsky, 2009), Caltech-UCSD Birds 200 (CUB200) (Welinder et al., 2010), Stanford-Cars (Cars) (Krause et al., 2013), Stanford-dogs (Dogs) (Khosla et al., 2011), and Tiny-Image Net (Tiny Im Net) (Deng et al., 2009). |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology for training, validation, and test sets). |
| Hardware Specification | Yes | All tests are conducted using NVIDIA Tesla V100 GPU with the same random seed. |
| Software Dependencies | No | The paper mentions using 'Geoopt optimizer (Kochurov et al., 2020)' but does not provide specific version numbers for software components or libraries. |
| Experiment Setup | Yes | We used the stochastic gradient descent (SGD) over 300 epochs, with a mini-batch of 64 and a momentum parameter of 0.9 for training. The learning rate schedule is the same for all experiments, starting at 0.1, then decaying by a factor of 10 after 150, then 225 epochs. |