Stochastic Interpolants with Data-Dependent Couplings
Authors: Michael Samuel Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we apply the framework to numerical experiments on Image Net, focusing on image inpainting and image super-resolution. and Table 2: FID for Inpainting Task. FID comparison between under two paradigms: a baseline, where ρ0 is a Gaussian with independent coupling to ρ1, and our data-dependent coupling detailed in Section 4.1. |
| Researcher Affiliation | Academia | 1Center for Cosmology and Particle Physics, New York University 2Courant Institute of Mathematical Sciences, New York University 3Center for Data Science, New York University. |
| Pseudocode | Yes | Algorithm 1 Training and Algorithm 2 Sampling (via forward Euler method) |
| Open Source Code | Yes | The code is available at https://github.com/interpolants/couplings. |
| Open Datasets | Yes | In our experiments, we set ρ1(x1) to correspond to Image Net (either 256 or 512). |
| Dataset Splits | Yes | In our experiments, we set ρ1 to correspond to Image Net (256 or 512), following prior work (Saharia et al., 2022; Ho et al., 2022a). and Table 3: FID-50k for Super-resolution, 64x64 to 256x256. FIDs for baselines taken from (Saharia et al., 2022; Ho et al., 2022a; Liu et al., 2023a). Model Train Valid |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instance specifications) were provided for the experimental setup. |
| Software Dependencies | No | The paper mentions using PyTorch, Lightning Fabric, Adam optimizer, U-net implementation, and the torchdiffeq library, but no specific version numbers are provided for these software components. |
| Experiment Setup | Yes | Additional specific experimental details may be found in Appendix B. [...] We use the following hyperparameters: Dim Mults: (1,1,2,3,4) Dim (channels): 256 Resnet block groups: 8 Leanred Sinusoidal Cond: True Learned Sinusoidal Dim: 32 Attention Dim Head: 64 Attention Heads: 4 Random Fourier Features: False. [...] We use Adam optimizer (Kingma & Ba, 2014), starting at learning rate 2e-4 with the Step LR scheduler which scales the learning rate by γ = .99 every N = 1000 steps. We use no weight decay. We clip gradient norms at 10, 000. |