Summary of Integrating Knn with Foundation Models For Adaptable and Privacy-aware Image Classification, by Sebastian Doerrich et al.
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification
by Sebastian Doerrich, Tobias Archut, Francesco Di Salvo, Christian Ledig
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitations of traditional deep learning models by introducing a novel approach that stores embeddings of underlying training data independently of model weights. This enables dynamic data modifications without retraining, making it ideal for addressing user data privacy concerns. The authors integrate a k-Nearest Neighbor (k-NN) classifier with a vision-based foundation model pre-trained on natural images, enhancing interpretability and adaptability. They share open-source implementations of a baseline method and their performance-improving contributions. Quantitative experiments confirm improved classification across established benchmark datasets and the method’s applicability to distinct medical image classification tasks. The approach shows great promise for bridging the gap between foundation models’ performance and data privacy challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making deep learning models more flexible and transparent. Right now, these models are stuck with the knowledge they learned from their training data, which can be a problem when we need to make changes or add new information. The authors found a way to store this knowledge separately, so that we can make changes without having to retrain the model. They also tested their approach on different types of images and showed that it works well. This could be really important for things like medical image classification, where we need to be able to adapt quickly to new information. |
Keywords
* Artificial intelligence * Classification * Deep learning * Image classification * Nearest neighbor
