Online Domain Adaptation
5 papers with code • 0 benchmarks • 0 datasets
Online Adaptation aims to tackle multiple domain shifts, occurring unpredictably during deployment in real applications and without clear boundaries between them.
Benchmarks
These leaderboards are used to track progress in Online Domain Adaptation
Most implemented papers
ATL: Autonomous Knowledge Transfer from Many Streaming Processes
It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain.
Online Domain Adaptation for Occupancy Mapping
Further, with the use of high-fidelity driving simulators and real-world datasets, we demonstrate how parameters of 2D and 3D occupancy maps can be automatically adapted to accord with local spatial changes.
Automatic Online Multi-Source Domain Adaptation
Knowledge transfer across several streaming processes remain challenging problem not only because of different distributions of each stream but also because of rapidly changing and never-ending environments of data streams.
Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions
theo2021/onda
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Unsupervised Domain Adaptation (UDA) aims at reducing the domain gap between training and testing data and is, in most cases, carried out in offline manner.
To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
MarcBotet/hamlet
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ICCV 2023
The goal of Online Domain Adaptation for semantic segmentation is to handle unforeseeable domain changes that occur during deployment, like sudden weather events.
