Search Results for author:
Found 6 papers, 3 papers with code
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration
By integrating weight standardization and channel attention in the backbone model, ANFR offers a novel and versatile approach to the challenge of statistical heterogeneity.
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection
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.
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Such a score function is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a pixel-wise anomaly score.
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
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 clients and a server.
Contribution Evaluation in Federated Learning: Examining Current Approaches
Federated Learning (FL) has seen increasing interest in cases where entities want to collaboratively train models while maintaining privacy and governance over their data.
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy.
