Reproducibility of "FDA: Fourier Domain Adaptation forSemantic Segmentation
Not on the map. Why?
Abstract (arXiv)
The following paper is a reproducibility report for "FDA: Fourier Domain Adaptation for Semantic Segmentation" published in the CVPR 2020 as part of the ML Reproducibility Challenge 2020. The original code was made available by the author. The well-commented version of the code containing all ablation studies performed derived from the original code along with WANDB integration is available at <github.com/thefatbandit/FDA> with proper instructions to execute experiments in README.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 12 references that are in the map. First posted on arXiv 30 Apr 2021.
Builds on
9 of 12 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
- 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
- 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
- 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,082
- 2016Playing for Data: Ground Truth from Computer Gamescited by 818
- 2020FDA: Fourier Domain Adaptation for Semantic Segmentationcited by 541
- 2019Bidirectional Learning for Domain Adaptation of Semantic Segmentationcited by 308
Built on by
Citing papers per year: 2022: 1.
1 paper.