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Found 10 papers, 4 papers with code
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.
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.
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
Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective
Matrix approximations are a key element in large-scale algebraic machine learning approaches.
Probabilistic classifiers with low rank indefinite kernels
Indefinite similarity measures can be frequently found in bio-informatics by means of alignment scores, but are also common in other fields like shape measures in image retrieval.
Transfer learning extensions for the probabilistic classification vector machine
Transfer learning is focused on the reuse of supervised learning models in a new context.
Complex-valued embeddings of generic proximity data
As an information preserving alternative, we propose a complex-valued vector embedding of proximity data.
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.
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.
Efficient Cross-Domain Federated Learning by MixStyle Approximation
With the advent of interconnected and sensor-equipped edge devices, Federated Learning (FL) has gained significant attention, enabling decentralized learning while maintaining data privacy.
