Summary of Improving Deep Learning with Prior Knowledge and Cognitive Models: a Survey on Enhancing Explainability, Adversarial Robustness and Zero-shot Learning, by Fuseinin Mumuni and Alhassan Mumuni
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning
by Fuseinin Mumuni, Alhassan Mumuni
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper reviews recent advancements in knowledge-informed and brain-inspired cognitive systems for developing robust adversarial defenses, explainable artificial intelligence (XAI), and zero-shot or few-shot learning. It highlights the limitations of deep learning models, which excel in specific domains but struggle with interpretability, generalizability, and vulnerability to attacks. The authors propose incorporating prior knowledge into deep learning frameworks and using brain-inspired cognition methods to enhance intelligent behavior, improve explainability, and achieve better adversarial robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make artificial intelligence more like the human brain. It talks about how AI systems can be designed to learn from experience and adapt to new situations without needing a lot of data. The authors also discuss how AI can be made more understandable and transparent, so we can see why it’s making certain decisions. They propose using “brain-inspired” approaches that mimic how the human brain works to develop more robust and efficient AI systems. |
Keywords
* Artificial intelligence * Deep learning * Few shot * Zero shot
