让我们继续前进:图像可计算模型和一个共同的模型评估方案是科学理解人类视觉的先决条件
James J DiCarlo1, Daniel L K Yamins2, Michael E Ferguson1
1Dept. of Brain and Cognitive Sciences, Quest for Intelligence, and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA dicarlo@mit.edu; https://dicarlolab.mit.edu mferg@mit.edu evelina9@mit.edu; https://evlab.mit.edu/ msch@mit.edu; https://mschrimpf.com/.
The Behavioral and brain sciences
|December 6, 2023
概括
尔斯和其他人. 批评深层人工神经网络 (ANN) 在没有提供替代方案或使用公共基准的情况下模拟人类视觉. 脑分数社区推进了透明的基准,用于将ANN与人类认知进行比较.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
- 认知科学是一种认知科学.
背景情况:
- 深度人工神经网络 (ANN) 是人类视觉的突出模型.
- 尔斯和其他人. 质疑ANN的有效性,并主张采用碎片化,特定现象的方法.
- 目前的方法缺乏计算模型和生物视觉之间的标准化,透明的比较.
研究的目的:
- 批评尔斯等人的方法. 关于人工神经网络 (ANN) 人类视觉模型.
- 倡导继续开发和使用全面的基准测试平台.
- 突出"脑分数"社区在建立模型与人之间的比较方面取得的进展.
主要方法:
- 对尔斯等人提出的论点的分析.
- 解释特定现象建模的局限性.
- 描述Brain-Score社区对透明基准测试的方法.
主要成果:
- 这是尔斯等人所倡导的方法. 被认为不利于科学进步.
- 公共基准测试平台对于稳健的模型评估至关重要.
- 脑分数社区正在扩大其用于模型对人类比较的基准套件.
结论:
- 一个统一的,基准驱动的方法优于碎片化,特定现象的建模来理解视觉.
- 持续开发透明的,社区驱动的基准对于推进计算神经科学至关重要.
- 脑分数计划为对人类认知进行人工智能模型严格评估提供了一条前进的道路.
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