Summary of Associative Memories in the Feature Space, by Tommaso Salvatori et al.
Associative Memories in the Feature Space
by Tommaso Salvatori, Beren Millidge, Yuhang Song, Rafal Bogacz, Thomas Lukasiewicz
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 presents an autoassociative memory model that retrieves data points based on their similarity in an embedding space rather than the raw pixel space. The current models fail to retrieve images even with mild corruption because they evaluate similarities in the pixel space, which lacks semantic information. The authors propose a network pretrained with a contrastive loss to compute effective embeddings for fast computation of similarity scores. They test this method on complex datasets such as CIFAR10 and STL10. Additionally, the paper relaxes the condition of storing the whole dataset by proposing a class of memory models that only stores low-dimensional semantic embeddings and uses them to retrieve similar memories. This proof-of-concept is demonstrated on the MNIST dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research improves a type of computer model called an autoassociative memory model. The current models are good at finding similarities in images, but they struggle when the image is changed slightly, like removing some pixels. To fix this problem, the authors suggest using a different way to compare images by converting them into smaller sets of numbers that capture their main features. This makes it faster and more accurate to find similar images. The authors test their method on several datasets and show that it can successfully retrieve similar images. |
Keywords
* Artificial intelligence * Contrastive loss * Embedding space
