Explainability of vision transformers: a comprehensive review and new perspectives

Key Points

• Explainability in vision transformers is crucial for trust and usability in machine learning applications.
• Key evidence reveals that improved attention mechanisms can enhance interpretability, leading to better model understanding.
• This review analyzes various explainability methods applied to vision transformers in neural networks.
• Enhancing explainability may foster broader adoption of vision transformers across diverse fields.