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Found 6 papers, 3 papers with code
Federated Learning -- Methods, Applications and beyond
In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress. While some domains like web analysis can benefit from this with only minor restrictions, other fields like in medicine with patient data are strongerregulated.
Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks
We addressed the first issue by the alignment of transferable spectral properties within an adversarial model to balance the focus between the easily transferable features and the necessary discriminatory features, while at the same time limiting the learning of domain-specific semantics by relevance considerations.
Ranked #9 on
Domain Adaptation
on ImageCLEF-DA
Transfer learning extensions for the probabilistic classification vector machine
Transfer learning is focused on the reuse of supervised learning models in a new context.
Reactive Soft Prototype Computing for Concept Drift Streams
The amount of real-time communication between agents in an information system has increased rapidly since the beginning of the decade.
Domain Adaptation via Low-Rank Basis Approximation
The presented approach finds a target subspace representation for source and target data to address domain differences by orthogonal basis transfer.
Low-Rank Subspace Override for Unsupervised Domain Adaptation
Current supervised learning models cannot generalize well across domain boundaries, which is a known problem in many applications, such as robotics or visual classification.
