Human Attention Benchmarks for Multi-Task Learning in Attention-Based Models

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: Can the human attention benchmark be used to improve the training of attention-based models through multi-task learning frameworks. Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: Can the human attention benchmark be used to improve the training of attention-based models through multi-task learning frameworks?

Autonomous literature synthesis. Automated review score: 7.5/10. Full text and citation available at Assignee Research.