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..
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction
Authors: Zeshuai Deng, Zhuokun Chen, Shuaicheng Niu, Thomas Li, Bohan Zhuang, Mingkui Tan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on newly synthesized corrupted DIV2K datasets with 8 different degradations and several real-world datasets, demonstrating that our SRTTA framework achieves an impressive improvement over existing methods with satisfying speed. |
| Researcher Affiliation | Academia | Zeshuai Deng1 Zhuokun Chen1 2 Shuaicheng Niu1 Thomas H. Li5 Bohan Zhuang3 Mingkui Tan1 2 4 1South China University of Technology, 2Pazhou Lab, 3ZIP Lab, Monash University, 4Key Laboratory of Big Data and Intelligent Robot, Ministry of Education, 5Peking University Shenzhen Graduate School |
| Pseudocode | Yes | Algorithm 1: The pipeline of the proposed Super-Resolution Test-Time Adaptation. |
| Open Source Code | Yes | The source code is available at https://github.com/Deng Zeshuai/SRTTA. |
| Open Datasets | Yes | Extensive experiments are conducted on newly synthesized corrupted DIV2K datasets with 8 different degradations and several real-world datasets, demonstrating that our SRTTA framework achieves an impressive improvement over existing methods with satisfying speed. The DIV2K [1] dataset. |
| Dataset Splits | Yes | Testing data. Following Image Net-C [18], we degraded 100 validation images from the DIV2K [1] dataset into eight domains. |
| Hardware Specification | Yes | To compare the inference times of different SR methods, we measure all methods on a TITAN XP with 12G graphics memory for a fair comparison. |
| Software Dependencies | No | The paper mentions software components such as Adam optimizer, EDSR, Diff JPEG (PyTorch implementation), ResNet-50, and OpenCV, but it does not specify version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | For the balance weight in Eqn. (6), we set α to 1. For the ratio of parameters to be frozen, we set the ρ to 0.50. For test-time adaptation, we use the Adam optimizer with the learning rate of 5e-5 for the pre-trained SR models. We set the batch size N to 32, and we randomly crop the test image into N patches of size 96x96 and 64x64 for x2 and x4 SR, and degrade them into second-order degraded patches. We perform S = 10 iterations of adaptation for each test image. |