Improving Robustness of Deep-Learning-Based Image Reconstruction
Authors: Ankit Raj, Yoram Bresler, Bo Li
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments using the proposed min-max scheme confirm convergence to this solution. We complement the theory by experiments on non-linear Compressive Sensing (CS) reconstruction by a deep neural network on two standard datasets, and, using anonymized clinical data, on a state-of-the-art published algorithm for low-dose x-ray CT reconstruction. |
| Researcher Affiliation | Academia | 1Coordinated Science Laboratory and Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign (UIUC) 2Department of Computer Science, UIUC. |
| Pseudocode | Yes | Algorithm 1 Algorithm for training at iteration T Input: Mini-batch samples (x T , y T ), GT 1, f T 1 Output: GT and f T |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | The MNIST dataset (Le Cun et al., 1998) consists of 28 28 gray-scale images of digits with 50, 000 training and 10, 000 test samples. The Celeb A dataset (Liu et al., 2015) consists of more than 200, 000 celebrity images. We used anonymized clinical CT images (Vannier, 2007) of size 512 512 884 for training & validation and 221 for evaluation. |
| Dataset Splits | Yes | The MNIST dataset (Le Cun et al., 1998) consists of 28 28 gray-scale images of digits with 50, 000 training and 10, 000 test samples. We randomly pick 160, 000 images for the training. Images from the 40, 000 held-out set are used for evaluation. We used anonymized clinical CT images (Vannier, 2007) of size 512 512 884 for training & validation and 221 for evaluation. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam Optimizer' and 'Astra toolbox (Van Aarle et al., 2016)' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We used the Adam Optimizer with β1 = 0.5, β2 = 0.999, learning rate of 10 4 and mini-batch size of 128, but divided into K = 4 parts during the update of G, described in the algorithm 1. Empirically, we found λ1 = 1 and λ2 = 0.1 in (6), gave the best performance in terms of robustness (lower ˆρ) for different perturbations. We found λ1 = 3 and λ2 = 1 in (6) gave the best robustness performance (lower ˆρ) for different perturbations. |