Integrating kNN with Foundation Models for Adaptable and Privacy-Aware
  Image Classification
Sebastian Doerrich Tobias Archut Francesco Di Salvo Christian Ledig
Traditional deep learning models implicity 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implementations of a previously unpublished baseline method as well as our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
https://github.com/TobArc/privacy-aware-image-classification-with-kNN.