Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Siomos, Vasilis"'
Federated learning is a decentralized collaborative training paradigm that preserves stakeholders' data ownership while improving performance and generalization. However, statistical heterogeneity among client datasets poses a fundamental challenge b
Externí odkaz:
http://arxiv.org/abs/2410.02006
Autor:
Marimont, Sergio Naval, Siomos, Vasilis, Baugh, Matthew, Tzelepis, Christos, Kainz, Bernhard, Tarroni, Giacomo
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by gener
Externí odkaz:
http://arxiv.org/abs/2407.06635
Autor:
Marimont, Sergio Naval, Baugh, Matthew, Siomos, Vasilis, Tzelepis, Christos, Kainz, Bernhard, Tarroni, Giacomo
Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize anomalies without relying on annotations, only leveraging a model trained on a dataset known to be free of anomalies. Diffusion models learn to modify inputs $x$ to increase
Externí odkaz:
http://arxiv.org/abs/2311.15453
Federated Learning (FL) is a collaborative training paradigm that allows for privacy-preserving learning of cross-institutional models by eliminating the exchange of sensitive data and instead relying on the exchange of model parameters between the c
Externí odkaz:
http://arxiv.org/abs/2311.14625
Federated Learning (FL) has seen increasing interest in cases where entities want to collaboratively train models while maintaining privacy and governance over their data. In FL, clients with private and potentially heterogeneous data and compute res
Externí odkaz:
http://arxiv.org/abs/2311.09856
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy. An established approach is to tokenize images and model the distribution of tokens with
Externí odkaz:
http://arxiv.org/abs/2307.14701