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Task and Explanation Network

arXiv 2401.01732 ↗First posted 3 Jan 2024cs.LG cs.AI cs.NEcited by 0

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Abstract (arXiv)

Explainability in deep networks has gained increased importance in recent years. We argue herein that an AI must be tasked not just with a task but also with an explanation of why said task was accomplished as such. We present a basic framework -- Task and Explanation Network (TENet) -- which fully integrates task completion and its explanation. We believe that the field of AI as a whole should insist -- quite emphatically -- on explainability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 12 references that are in the map. First posted on arXiv 3 Jan 2024.

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2 of 12 references.

References · 2

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608

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