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..
Pareto Domain Adaptation
Authors: fangrui lv, Jian Liang, Kaixiong Gong, Shuang Li, Chi Harold Liu, Han Li, Di Liu, Guoren Wang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on image classification and semantic segmentation benchmarks demonstrate the effectiveness of Pareto DA. Our code is available at https://github.com/BIT-DA/Pareto DA. |
| Researcher Affiliation | Collaboration | 1 Beijing Institute of Technology, China 2Alibaba Group, China 1 EMAIL, EMAIL 2 EMAIL |
| Pseudocode | Yes | Algorithm 1 Update Equations for Pareto DA |
| Open Source Code | Yes | Our code is available at https://github.com/BIT-DA/Pareto DA. |
| Open Datasets | Yes | Office-31 [40] is a typical benchmark for cross-domain object classification... Office-Home [48] is a more difficult benchmark... Vis DA-2017 [37] is a large-scale synthetic-to-real dataset... Cityscapes [8] is a real-world semantic segmentation dataset... GTA5 [38] includes 24,966 game screenshots... |
| Dataset Splits | Yes | In practice, we randomly split 10% data from the original target set as the validation set and the rest 90% are taken as the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments (e.g., GPU/CPU models, memory details). |
| Software Dependencies | No | All experiments are implemented via Py Torch [36], and we adopt the common-used stochastic gradient descent (SGD) optimizer with momentum 0.9 and weight decay 1e-4 for all experiments. (No version numbers are provided for PyTorch or other software dependencies). |
| Experiment Setup | Yes | All experiments are implemented via Py Torch [36], and we adopt the common-used stochastic gradient descent (SGD) optimizer with momentum 0.9 and weight decay 1e-4 for all experiments. |