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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Region-Adaptive Dense Network for Efficient Motion Deblurring
Authors: Kuldeep Purohit, A. N. Rajagopalan11882-11889
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive comparisons with prior art on benchmark dynamic scene deblurring datasets clearly demonstrate the superiority of the proposed networks via significant improvements in accuracy and speed, enabling almost real-time deblurring. |
| Researcher Affiliation | Academia | Kuldeep Purohit, A. N. Rajagopalan Indian Institute of Technology Madras, India EMAIL, EMAIL |
| Pseudocode | No | The paper provides architectural diagrams and descriptions but does not include pseudocode or explicit algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | we perform training and evaluation of our network on the dynamic scene deblurring benchmark (Nah, Kim, and Lee 2017) |
| Dataset Splits | Yes | Following the same train-test split as in (Nah, Kim, and Lee 2017), we use 2103 pairs for training and 1111 pairs for evaluation. |
| Hardware Specification | Yes | We conduct our experiments on a PC with Intel Xeon E5 CPU, 256 GB RAM and an NVIDIA Titan X GPU. |
| Software Dependencies | No | The paper mentions optimizers and frameworks indirectly through citations, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Training is done for 1 × 10^6 iterations using Adam optimizer with learning rate 0.0001 on patches of 256 × 256 and batch-size of 16. |