Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models
Authors: Dongzhuo Li
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our technique on three inversion tasks: compressive-sensing MRI, image deblurring, and eikonal tomography (a nonlinear PDE-constrained inverse problem) using two representative deep generative models: Style GAN2 and Glow. Our approach achieves state-of-the-art performance in terms of accuracy and consistency. and 4 EXPERIMENTS We consider three representative inversion problems for testing: compressive sensing MRI, image deblurring, and eikonal traveltime tomography. |
| Researcher Affiliation | Industry | Dongzhuo Li Exxon Mobil Technology & Engineering Company dongzhuo.li@exxonmobil.com |
| Pseudocode | Yes | Algorithm 1: ICA Layer and Algorithm 2: Power Transformation Layer and Algorithm 3: Lambert W FX Layer with the Iterative Generalized Method of Moments (IGMM). |
| Open Source Code | Yes | The implementation is available here. |
| Open Datasets | Yes | For MRI and eikonal tomography, we used synthetic brain images as inversion targets and used the pre-trained Style GAN2 weights from Kelkar & Anastasio (2021) (trained on data from the databases of fast MRI (Zbontar et al., 2018; Knoll et al., 2020), TCIA-GBM (Scarpace et al., 2016), and OASIS-3 (La Montagne et al., 2019)) for regularization. We used the test split of the Celeb A-HQ dataset (Karras et al., 2018) for deblurring, and the DGM is a Glow network trained on the training split. |
| Dataset Splits | Yes | We split the 30000 images from Celeb A-HQ into the subsets of training (24183 images), validation (2993 images), and testing (2824 images) following the original splits from Celeb A (Liu et al., 2015). |
| Hardware Specification | Yes | All training was conducted using 8 32 GB Nvidia V100 GPUs with a batch size of 64. |
| Software Dependencies | No | The paper mentions software like SciPy and implies PyTorch (via `torch.roll`), but does not provide specific version numbers for these or other key software dependencies. |
| Experiment Setup | Yes | We used the LBFGS (Nocedal & Wright, 2006) optimizer in all experiments except TV, noise regularization, and CSGM-w, which use FISTA (Beck & Teboulle, 2009) or ADAM (Kingma & Ba, 2015). The temperature was set to 1.0 for Style GAN2 and 0.7 for Glow. For the hyper-parameters of the Glow networks, we used 4 multi-scale levels and 32 flow-steps, and we only used additive coupling layers. All training was conducted using 8 32 GB Nvidia V100 GPUs with a batch size of 64. We used the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 10 4, as well as β1 = 0.9 and β2 = 0.99. |