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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Noise-Free Optimization in Early Training Steps for Image Super-resolution
Authors: MinKyu Lee, Jae-Pil Heo
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed method can effectively enhance the stability of vanilla training, leading to overall performance gain. Codes are available at github.com/2minkyulee/ECO. |
| Researcher Affiliation | Academia | Min Kyu Lee, Jae-Pil Heo* Sungkyunkwan University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Procedures are described in narrative text. |
| Open Source Code | Yes | Codes are available at github.com/2minkyulee/ECO. |
| Open Datasets | Yes | We validate the effectiveness of our method on benchmark datasets: Set5 (Bevilacqua et al. 2012), Set14 (Zeyde, Elad, and Protter 2010), BSD100 (Martin et al. 2001), Urban100 (Huang, Singh, and Ahuja 2015) and Manga109 (Matsui et al. 2017). |
| Dataset Splits | No | The paper mentions 'Validation results' and uses benchmark datasets that typically have predefined splits, but it does not explicitly provide specific details like percentages, sample counts, or explicit statements about standard split usage for reproducibility in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like ReLU and implicitly deep learning frameworks, but it does not specify exact version numbers for any key software components or libraries (e.g., Python version, PyTorch version, CUDA version). |
| Experiment Setup | Yes | Figure 3 shows 'Ours (lr=1e-4) Ours (lr=2e-4) KD (lr=1e-4) Vanilla (lr=1e-4)', indicating specific learning rates. Figure 4 shows results across 'mini-batch sizes of 2, 4, 8, and 16'. |