Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Solving Inverse Problems via Diffusion Optimal Control
Authors: Henry Li, Marcus Pereira
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then evaluate our method against a selection of neural inverse problem solvers, and establish a new baseline in image reconstruction with inverse problems1. |
| Researcher Affiliation | Collaboration | Henry Li Yale University EMAIL Marcus Pereira Bosch Center for Artificial Intelligence EMAIL |
| Pseudocode | Yes | Algorithm 1 Diffusion Optimal Control Input: λ, T, y, x T Initialize ut, kt, Kt as 0 for t = 1 . . . T, {x t}T t=0 as uncontrolled dynamics for iter = 1 to num_iters do Vx, Vxx x0 log p(y|x0), 2 x0 log p(y|x0) Initialize derivatives of V (xt, t) for t = 1 to T do Compute kt, Kt, Vx, Vxx See Eqs. (13), (14) end for for t = T to 1 do xt 1 h(xt, λkt + Kt(xt x t)) Update xt 1 with new ut x t xt end for end for |
| Open Source Code | No | The authors will release code upon acceptance. |
| Open Datasets | Yes | We validate our results on the high resolution human face dataset FFHQ 256 256 Karras et al. [2019]. |
| Dataset Splits | Yes | To fairly compare between all models, all methods use the model weights from Chung et al. [2023a], which are trained on 49K FFHQ images, with 1K images left as a held-out set for evaluation. |
| Hardware Specification | Yes | Experiments can be run on any GPU A4000 or later. |
| Software Dependencies | No | The paper mentions 'Adam optimizer Kingma and Ba [2014]' and 'standard automatic differentiation libraries (e.g. torch.func.vjp)' but does not provide specific version numbers for these software dependencies, only the underlying algorithm for Adam. |
| Experiment Setup | Yes | Further hyperparameters can be found in Table 2. For the classifier p(y|x) in MNIST class-guided classification, we use a simple convolutional neural network with two convolutional layers and two MLP layers, trained on the entire MNIST dataset. |