5 papers
Evaluating the Interpretability of Sparse Autoencoders with Concept Annotations
Jonas Klotz, Cassio F. Dantas, Pallavi Jain +2
Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy met…
FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing
BarıŠBüyüktaÅ, Jonas Klotz, Begüm Demir
Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients), where each client stores data locally and onl…
A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification
Tom Burgert, Kai Norman Clasen, Jonas Klotz +2
The development of supervised deep learning-based methods for multi-label scene classification (MLC) is one of the prominent research directions in remote sensing (RS). However, co…
On the Effectiveness of Methods and Metrics for Explainable AI in Remote Sensing Image Scene Classification
Jonas Klotz, Tom Burgert, Begüm Demir
The development of explainable artificial intelligence (xAI) methods for scene classification problems has attracted great attention in remote sensing (RS). Most xAI methods and th…
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classification
Jonas Klotz, BarıŠBüyüktaÅ, Begüm Demir
Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a global model by exchanging only model updates with the centra…