Summary of Quantum-tunnelling Deep Neural Network For Optical Illusion Recognition, by Ivan S. Maksymov
Quantum-tunnelling deep neural network for optical illusion recognition
by Ivan S. Maksymov
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Neural and Evolutionary Computing (cs.NE); Physics and Society (physics.soc-ph); Quantum Physics (quant-ph)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, a deep neural network (DNN) architecture called Quantum Tunnelling (QT)-DNN is introduced. This novel approach processes information using the quantum tunnelling effect, which allows particles to transmit through high potential barriers. The authors demonstrate that QT-DNN can recognize optical illusions like humans, specifically simulating human perception of the Necker cube and Rubin’s vase. They argue that QT-based activation functions outperform those optimized for modern machine vision applications and show a connection between QT-DNN and biology-inspired DNNs and quantum information processing models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new deep neural network (DNN) called Quantum Tunnelling (QT)-DNN. It uses the same idea that helps particles go through barriers, but for images! The researchers tested it with tricky optical illusions, like seeing different shapes in the same picture. They found that this special DNN can recognize these illusions just like humans do. This new way of processing information is better than what we use now and might help us learn more about how our brains work. |
Keywords
* Artificial intelligence * Neural network
