Summary of Learning From Memory: Non-parametric Memory Augmented Self-supervised Learning Of Visual Features, by Thalles Silva et al.
Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features
by Thalles Silva, Helio Pedrini, Adín Ramírez Rivera
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The proposed method introduces a novel approach to improving the training stability of self-supervised learning (SSL) methods by leveraging a non-parametric memory of seen concepts. The novel technique involves augmenting a neural network with a memory component to stochastically compare current image views with previously encountered concepts. Additionally, stochastic memory blocks are introduced to regularize training and enforce consistency between image views. The proposed approach is extensively benchmarked on various vision tasks, including linear probing, transfer learning, low-shot classification, and image retrieval on several datasets. Experimental results demonstrate the effectiveness of the proposed method in achieving stable SSL training without additional regularizers while learning highly transferable representations and requiring less computing time and resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make self-supervised learning (SSL) work better by using a special kind of memory. The idea is to compare new images with old ones, which helps the network learn more stable and useful features. This approach also makes training faster and uses fewer computer resources. It’s tested on many different tasks and datasets, and the results show that it works really well. |
Keywords
» Artificial intelligence » Classification » Neural network » Self supervised » Transfer learning
