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].
Learning Provably Robust Estimators for Inverse Problems via Jittering
Authors: Anselm Krainovic, Mahdi Soltanolkotabi, Reinhard Heckel
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we examine jittering empirically via training deep neural networks (U-nets) for natural image denoising, deconvolution, and accelerated magnetic resonance imaging (MRI). The results show that jittering significantly enhances the worst-case robustness, but can be suboptimal for inverse problems beyond denoising. |
| Researcher Affiliation | Academia | Anselm Krainovic Technical University of Munich EMAIL Mahdi Soltanolkotabi University of Southern California EMAIL Reinhard Heckel Technical University of Munich EMAIL |
| Pseudocode | No | The paper does not contain any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | The repository at https://github.com/MLI-lab/robust_ reconstructors_via_jittering contains the code to reproduce all results in the main body of this paper. |
| Open Datasets | Yes | We obtain train and validation datasets {(x1, y1), . . . , (x N, y N)} of sizes 34k and 4k, respectively, from colorized images of size n = 128 128 3 generated by randomly cropping and flipping Image Net images. We use the fast MRI singlecoil knee dataset (Zbontar et al., 2018), which contains the images x and fully sampled measurements (M = I). |
| Dataset Splits | Yes | We obtain train and validation datasets {(x1, y1), . . . , (x N, y N)} of sizes 34k and 4k, respectively, from colorized images of size n = 128 128 3 generated by randomly cropping and flipping Image Net images. We process it by random subsampling at acceleration factor 4 and obtain train, validation and test datasets with approximately 31k, 3.5k and 7k slices, respectively. |
| Hardware Specification | Yes | The experimental results presented in this paper were computed using on-premise infrastructure equipped with Nvidia RTX A6000 GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch s Adam optimizer' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Throughout, we use Py Torch s Adam optimizer with learning rate 10 3 and batch size 50 for natural images, and 10 2 and 1 for MRI data. As perturbation levels, we consider values within the practically interesting regime of ϵ2/E h Ax 2 2 i < 0.3 for natural images and 0.03 for MRI data. We use stochastic gradient descent (SGD) with learning rate 10 2, momentum 0.9 and batch size 100. |