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Found 8 papers, 3 papers with code
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning
This paper addresses the challenge of online multi-source domain adaptation (MSDA) in transfer learning, a scenario where one needs to adapt multiple, heterogeneous source domains towards a target domain that comes in a stream.
Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation
Multi-Source Domain Adaptation (MSDA) is a challenging scenario where multiple related and heterogeneous source datasets must be adapted to an unlabeled target dataset.
Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport
In this paper, we tackle Multi-Source Domain Adaptation (MSDA), a task in transfer learning where one adapts multiple heterogeneous, labeled source probability measures towards a different, unlabeled target measure.
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
The proposed framework, FedDaDiL, tackles the resulting challenge through dictionary learning of empirical distributions.
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning
To solve it, we adapt previous works in the MSDA literature, such as Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD method Distribution Matching.
Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process
In system monitoring, automatic fault diagnosis seeks to infer the systems' state based on sensor readings, e. g., through machine learning models.
Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions.
Recent Advances in Optimal Transport for Machine Learning
This is rooted in its rich history and theory, and has offered new solutions to different problems in machine learning, such as generative modeling and transfer learning.
