On Investigating the Conservative Property of Score-Based Generative Models
Authors: Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Chun-Yi Lee
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, our experimental results on the CIFAR10, CIFAR-100, Image Net, and SVHN datasets validate the effectiveness of QCSBMs. |
| Researcher Affiliation | Collaboration | 1Elsa Lab, Department of Computer Science, National Tsing Hua University, Taiwan. 2NVIDIA AI Technology Center, NVIDIA Corporation. |
| Pseudocode | Yes | Algorithm 1 Training Procedure of QCSBM |
| Open Source Code | Yes | The code implementation for the experiments is provided in the following repository: https://github.com/chen-hao-chao/qcsbm. |
| Open Datasets | Yes | CIFAR-10, CIFAR-100 (Krizhevsky & Hinton, 2009), Image Net-32x32 (Van Oord et al., 2016), and SVHN (Netzer et al., 2011) datasets. |
| Dataset Splits | No | The paper specifies 'training and test sets' with their sizes but does not mention explicit validation splits or percentages. |
| Hardware Specification | Yes | The results are evaluated on a single NVIDIA V100 GPU with 32 GB memory, and the batch size is fixed at 32. |
| Software Dependencies | No | The paper mentions 'Pytorch framework' and 'scipy.integrate.solve ivp library' but does not specify their version numbers. |
| Experiment Setup | Yes | The SBMs s U and s C are trained using the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 7.5 10 4 and a batch size of 5, 000. The balancing factor λ is fixed to 0.1. The maximal and minimal noise scales σmax and σmin are set to 3 and 0.1, respectively. |