Reproducibility of "FDA: Fourier Domain Adaptation forSemantic Segmentation
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.
Code (1)
Tasks
Domain AdaptationSemantic SegmentationUnsupervised Domain AdaptationSimilar Papers 제목 키워드 기반
FDA: Fourier Domain Adaptation for Semantic Segmentation
We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other. We illustrate t…
Domain AdaptationSegmentationSemantic SegmentationUnsupervised Domain AdaptationFVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation
Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model usi…
Domain AdaptationImage SegmentationMedical Image SegmentationSegmentation+3AH-OCDA: Amplitude-based Curriculum Learning and Hopfield Segmentation Model for Open Compound Domain Adaptation
Open compound domain adaptation (OCDA) is a practical domain adaptation problem that consists of a source domain, target compound domain, and unseen open domain. In this problem, the absence of domain labels and pixel-le…
Domain AdaptationSegmentationSemantic SegmentationCurriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation
Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems. Medical data collection normally involves different scanners, pro…
Domain AdaptationImage SegmentationMedical Image SegmentationSegmentation+2A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment
Domain adaptation methods in machine learning deal with the domain shift issue by aligning source and target data representation. This paper proposes a novel domain adaptation method for semantic segmentation that exploi…
Depth EstimationDomain AdaptationSegmentationSemantic Segmentation+2