What does guidance do? A fine-grained analysis in a simple setting
Authors: Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, Jianfeng Lu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to verifying these results empirically in synthetic settings, we also show how our theoretical insights can offer useful prescriptions for practical deployment. 3 Experiments Here we empirically verify the guidance dynamics predicted by Theorems 1 and 2. |
| Researcher Affiliation | Academia | Muthu Chidambaram Duke University muthu@cs.duke.edu Khashayar Gatmiry MIT gatmiry@mit.edu Sitan Chen Harvard University sitan@seas.harvard.edu Holden Lee Johns Hopkins University hlee283@jhu.edu Jianfeng Lu Duke University jianfeng@math.duke.edu |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | We include code in the supplementary material for recreating the numerical experiments in the paper. |
| Open Datasets | Yes | Simpler image datasets are known to be close to linearly separable in particular, MNIST. To conduct experiments on Image Net, we use the classifier-guided Image Net models available from [13]. |
| Dataset Splits | No | The paper mentions training models (e.g., 'training the guidance model of [28] for 40 epochs' for MNIST), but it does not explicitly specify the training/validation/test dataset splits used for these models, nor does it describe new training with such splits. It often uses pre-existing models or models trained by external works. |
| Hardware Specification | Yes | All experiments in this section were conducted on a single A5000 GPU. |
| Software Dependencies | No | The paper mentions using 'Jax [2]' and 'Py Torch [27]' for experiments but does not provide specific version numbers for these software dependencies in the main text. |
| Experiment Setup | Yes | For solving, we use 1000 evaluation steps and take T = 10... For sampling, we use DDPM [18] with 400 time steps and a linear noise schedule, and we found that training the guidance model of [28] for 40 epochs was sufficient to generate high quality samples. For sampling, we use DDIM [32] with 25 steps. |