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].
A Bridging Framework for Model Optimization and Deep Propagation
Authors: Risheng Liu, Shichao Cheng, xiaokun liu, Long Ma, Xin Fan, Zhongxuan Luo
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify our theoretical results and demonstrate the superiority of PODM against these state-of-the-art approaches. |
| Researcher Affiliation | Academia | Risheng Liu1,2 , Shichao Cheng3, Xiaokun Liu1, Long Ma1, Xin Fan1,2, Zhongxuan Luo2,3 1International School of Information Science & Engineering, Dalian University of Technology 2Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province 3School of Mathematical Science, Dalian University of Technology |
| Pseudocode | No | The paper describes the iterative processes and model components but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link for the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We plotted the iteration behaviors of PODM on example images from the commonly used Set5 super-resolution benchmark [4] and compared it with the most popular numerical solvers (e.g., FISTA [3]) and the recently proposed representative network based iteration methods (e.g., IRCNN [33]). We ๏ฌrst conducted experiments on the most widely used Levin et al. benchmark [15], with 32 blurry images of size 255 255. We also evaluated all these compared methods on the more challenging Sun et al. benchmark [28], which includes 640 blurry images with 1% Gaussian noises, sizes range from 620 1024 to 928 1024. |
| Dataset Splits | No | The paper mentions using common benchmark datasets but does not explicitly state the training, validation, or test data splits, such as percentages, sample counts, or specific splitting methodologies. |
| Hardware Specification | Yes | All the experiments are conducted on a PC with Intel Core i7 CPU @ 3.6 GHz, 32 GB RAM and an NVIDIA Ge Force GTX 1060 GPU. |
| Software Dependencies | No | The paper mentions general architectural components like 'CNN architecture' and 'multilayer perceptron' and references 'ELU [6] activations', but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | No | The paper describes the network architecture (e.g., '6 convolutional layers', 'ELU activations') and some settings like 'H = ยตI/2 with ยต = 1e 2' for PODM, but does not provide specific hyperparameter values like learning rates, batch sizes, or optimizer settings needed for full experimental reproducibility. |