8 citations · 11 across the 5 of their papers we have counts for
5 papers · 1 filter
FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation
Haokun Chen, Ahmed Frikha, Denis Krompass +2
Federated Learning (FL) is a decentralized learning paradigm, in which multiple clients collaboratively train deep learning models without centralizing their local data, and hence…
Towards Data-Free Domain Generalization
Ahmed Frikha, Haokun Chen, Denis Krompaß +2
In this work, we investigate the unexplored intersection of domain generalization (DG) and data-free learning. In particular, we address the question: How can knowledge contained i…
Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption
Ahmed Frikha, Denis Krompaß, Volker Tresp
Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts. We address the challenging domain g…
ARCADe: A Rapid Continual Anomaly Detector
Ahmed Frikha, Denis Krompaß, Volker Tresp
Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored. The present work addresses a le…
Few-Shot One-Class Classification via Meta-Learning
Ahmed Frikha, Denis Krompaß, Hans-Georg Köpken +1
Although few-shot learning and one-class classification (OCC), i.e., learning a binary classifier with data from only one class, have been separately well studied, their intersecti…