Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
Authors: Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach in settings from 2D data to high-resolution text-to-image generation. and 5 Experiments. |
| Researcher Affiliation | Collaboration | 1MIT 2Google Deepmind. |
| Pseudocode | Yes | Algorithm 1 Annealed MCMC |
| Open Source Code | No | Project webpage: https://energy-based-model.github.io/reduce-reuse-recycle/. This is a project overview page, not an explicit code repository or statement of code release for the methodology. |
| Open Datasets | Yes | We train our models on a dataset of images containing between 1 and 5 examples of various shapes taken from CLEVR (Johnson et al., 2017). and Next, we train unconditional diffusion models and a noise-conditioned classifier on Image Net. |
| Dataset Splits | No | The paper mentions using CLEVR and Image Net datasets but does not specify exact train/validation/test split percentages, sample counts, or detailed splitting methodology. |
| Hardware Specification | Yes | 10 minutes on a 8 TPUv2 cores, 8 hours on 8 TPUv2 cores, 3 days on 16 TPUv2 cores, one week on an internal text/image dataset consisting of 400 million images using 32 TPUv3 cores. |
| Software Dependencies | No | The paper mentions software like the 'Adam optimizer' but does not provide specific version numbers for any key software components or libraries. |
| Experiment Setup | Yes | For synthetic datasets, we train both score and energy based diffusion models using a small residual MLP model with 4 residual blocks, with a internal hidden dimension of 128 dimensions. We train models for 15000 iterations (10 minutes on a 8 TPUv2 cores) using the Adam optimizer with learning rate of 1e-3, and train diffusion models on 100 discrete timesteps with linear schedule of β values. |