Summary of Self-supervised Learning Of Color Constancy, by Markus R. Ernst et al.
Self-Supervised Learning of Color Constancy
by Markus R. Ernst, Francisco M. López, Arthur Aubret, Roland W. Fleming, Jochen Triesch
First submitted to arxiv on: 11 Apr 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 This paper presents a study on color constancy (CC) in neural networks trained using self-supervised learning. The researchers aim to understand how CC develops in the visual system, particularly during childhood. To achieve this, they train a network through an invariance learning objective, presenting objects under varying lighting conditions. The goal is to map subsequent views of the same object onto similar latent representations, resulting in representations that are largely invariant to illumination changes. This self-supervised approach offers a plausible explanation for how CC emerges during human cognitive development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how our brains learn to recognize colors despite changing lighting conditions. The researchers created a computer model that learns by itself, without being explicitly taught, to identify objects under different lighting conditions. They achieved this by presenting the model with images of the same object in various lighting scenarios, and asking it to group similar images together. This self-learning approach shows how our brains might develop color constancy during childhood. |
Keywords
* Artificial intelligence * Self supervised
