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..
Probabilistic Contrastive Learning for Domain Adaptation
Authors: Junjie Li, Yixin Zhang, Zilei Wang, Saihui Hou, Keyu Tu, Man Zhang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to validate the effectiveness of PCL and observe consistent performance gains on five tasks, i.e., Unsupervised/Semi-Supervised Domain Adaptation (UDA/SSDA), Semi-Supervised Learning (SSL), UDA Detection and Semantic Segmentation. |
| Researcher Affiliation | Academia | 1Beijing University of Posts and Telecommunications 2University of Science and Technology of China 3Beijing Normal University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/ljjcoder/Probabilistic Contrastive-Learning. |
| Open Datasets | Yes | We evaluate our method on two standard UDA semantic segmentation tasks: GTA5 [Richter et al., 2016] Cityscapes [Cordts et al., 2016] and SYNTHIA [Ros et al., 2016] Cityscapes. |
| Dataset Splits | No | The paper refers to standard benchmarks (e.g., Domain Net, Office-Home, CIFAR-100) and specific settings like "3-shot" or "1-shot" for semi-supervised tasks, but it does not provide explicit numerical or percentage-based train/validation/test splits, nor does it cite specific predefined split methodologies. |
| Hardware Specification | Yes | Notably, the training cost of our method is much lower than CPSL-D (PCL: 1*3090, 5 days v.s. CPSL-D: 4*V100, 11 days). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For the hyperparameter in PCL, we set s = 20 in all experiments. [...] For the hyperparameter in PCL, we set s = 7 in all experiments. |