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Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification
Doerrich, Sebastian; Archut, Tobias; Di Salvo, Francesco; u. a. (2024): Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification, in: Bamberg: Otto-Friedrich-Universität.
Faculty/Chair:
By:
Doerrich, Sebastian; ...
Publisher Information:
Year of publication:
2024
Pages:
Source/Other editions:
2024 IEEE International Symposium on Biomedical Imaging (ISBI) / IEEE, 2024, S. 1–5.
Year of first publication:
2024
Language:
English
Abstract:
Traditional deep learning models implicitly encode knowledge limiting their transparency and ability to adapt to data changes. Yet, this adaptability is vital for addressing user data privacy concerns. We address this limitation by storing embeddings of the underlying training data independently of the model weights, enabling dynamic data modifications without retraining. Specifically, our approach integrates the k-Nearest Neighbor (k-NN) classifier with a vision-based foundation model, pre-trained self-supervised on natural images, enhancing interpretability and adaptability. We share open-source implementations of a previously unpublished baseline method as well as our performance-improving contributions. Quantitative experiments confirm improved classification across established benchmark datasets and the method’s applicability to distinct medical image classification tasks. Additionally, we assess the method’s robustness in continual learning and data removal scenarios. The approach exhibits great promise for bridging the gap between foundation models’ performance and challenges tied to data privacy. The source code is available at github.com/TobArc.
GND Keywords: ; ; ;
Neuronales Netz
Klassifikator <Informatik>
Maschinelles Lernen
Explainable Artificial Intelligence
Keywords: ; ; ; ;
k-NN classifier
continual learning
transfer learning
few-shot classification
explainability
DDC Classification:
RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Type:
Conferenceobject
Activation date:
November 22, 2024
Permalink
https://fis.uni-bamberg.de/handle/uniba/104752