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..
Differential Alignment for Domain Adaptive Object Detection
Authors: Xinyu He, Xinhui Li, Xiaojie Guo
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on widely-used DAOD datasets together with ablation studies are conducted to demonstrate the efficacy of our proposed method and reveal its superiority over other SOTA alternatives. |
| Researcher Affiliation | Academia | Xinyu He, Xinhui Li, Xiaojie Guo* College of Intelligence and Computing, Tianjin University, Tianjin, China xy EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical equations, but it does not include a clearly labeled pseudocode block or algorithm section. |
| Open Source Code | Yes | Code https://github.com/Estrella-Xyu/Differential-Alignment-for-DAOD |
| Open Datasets | Yes | Cityscapes (Cordts et al. 2016) comprises 2,975 training images and 500 validation images, covering various urban environments and traffic conditions. [...] Foggy Cityscapes (Sakaridis et al. 2018) is a synthetic dataset rendered from Cityscapes with three levels of foggy density (0.005, 0.01, 0.02). [...] Sim10k (Johnson-Roberson et al. 2016) contains 10,000 images rendered from GTA engine. [...] BDD100k-daytime (Yu et al. 2020) is a subset of the larger BDD100k dataset, specially designed for daytime scenarios. |
| Dataset Splits | Yes | Cityscapes (Cordts et al. 2016) comprises 2,975 training images and 500 validation images, covering various urban environments and traffic conditions. [...] BDD100k-daytime (Yu et al. 2020) [...] It contains 36,728 training images and 5,258 validation images |
| Hardware Specification | Yes | Our implementation is based on the Py Torch framework and the model is trained on 4 NVIDIA RTX3090 GPUs with 24 GB of memory each. |
| Software Dependencies | No | The paper mentions "Py Torch framework" but does not specify a version number for PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | We optimize the network using the SGD optimizer with a momentum of 0.9. The initial learning rate is set to 0.01 and decreases in the final iteration. We use a batch size of 32, consisting of 16 labeled source images and 16 unlabeled target ones, and train the network for 25k iterations in total, including 10,000 iterations for burn-in and 15,000 iterations for teacher-student mutual learning. [...] In our experiments, setting α to 0.9996 works sufficiently well. [...] In our experiments, we set λ as 0.01 by default. |