Summary of Towards Flexible Perception with Visual Memory, by Robert Geirhos et al.
Towards flexible perception with visual memory
by Robert Geirhos, Priyank Jaini, Austin Stone, Sourabh Medapati, Xi Yi, George Toderici, Abhijit Ogale, Jonathon Shlens
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel approach to training neural networks is proposed, which combines the representational power of deep learning with the flexibility of a database. The method decomposes image classification into image similarity and search, allowing for flexible data addition and removal across scales. This explicit visual memory offers interpretable decision-making and can be controlled through intervention. The paper demonstrates the benefits of this approach and aims to contribute to a conversation on knowledge representation in deep vision models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to remember all your favorite memories from childhood, but they’re stored in a huge library with millions of books. Each book represents a single memory, and finding one specific memory would be like searching through the entire library! This paper explores a new way to store and retrieve memories (or “data”) using a combination of deep learning and databases. It allows us to add or remove memories easily, and even understand why we’re making certain decisions. The goal is to create a more flexible and interpretable way to represent knowledge in neural networks. |
Keywords
» Artificial intelligence » Deep learning » Image classification
