Solving Inverse Problems with a Flow-based Noise Model
Authors: Jay Whang, Qi Lei, Alex Dimakis
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate the efficacy of our method on various inverse problems, including compressed sensing with quantized measurements and denoising with highly structured noise patterns. We also present initial theoretical recovery guarantees for solving inverse problems with a flow prior. |
| Researcher Affiliation | Academia | Jay Whang 1 Qi Lei 2 Alexandros G. Dimakis 3 1Dept. of Computer Science, UT Austin, TX, USA 2Dept. of Electrical and Computer Engineering, Princeton University, NJ, USA 3Dept. of Electrical and Computer Engineering, UT Austin, TX, USA. |
| Pseudocode | No | The paper does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We trained multi-scale Real NVP models on two image datasets MNIST and Celeb A-HQ (Le Cun et al., 1998; Liu et al., 2015). |
| Dataset Splits | No | The paper mentions running experiments on the 'test set' and refers to 'trained models' implying training data. However, it does not explicitly specify the proportions or sizes of training, validation, or test splits (e.g., '80/10/10 split', '70% training, 15% validation, 15% test') or reference predefined splits for reproducibility beyond stating the test set size. |
| Hardware Specification | No | The paper mentions 'computing resources from TACC' in the Acknowledgements but does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that would be needed to replicate the experiments. |
| Experiment Setup | Yes | To remedy this, we use a smoothed version of the model density p G(x)β where β 0 is the smoothing parameter... Thus the loss we minimize becomes LMAP(z; y, β) = log p (y f(G(z))) β log p G(G(z)) (10) |