Pareto Domain Adaptation
Authors: fangrui lv, Jian Liang, Kaixiong Gong, Shuang Li, Chi Harold Liu, Han Li, Di Liu, Guoren Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {fangruilv,kxgong,shuangli,wanggrbit}@bit.edu.cn, liuchi02@gmail.com 2 {xuelang.lj,lihan.lh,wendi.ld}@alibaba-inc.com |
| 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. |