视觉皮层的模型对分布样本的概括有多好?
Yifei Ren1, Pouya Bashivan1,2,3
1Department of Computer Science, McGill University, Montreal, Canada.
PLoS computational biology
|May 31, 2024
概括
深度神经网络 (DNN) 在模拟灵长类视觉皮层活动方面表现有前途. 然而,改善DNN对象识别并不能保证对新兴刺激进行更好的神经预测.
科学领域:
- 计算神经科学是一种神经科学.
- 计算机视觉 计算机视觉
- 神经科学是一个神经科学.
背景情况:
- 深度神经网络 (DNN) 表现出类似于灵长类视觉皮层反应的单元活动.
- DNN活动的线性模型预测神经元对自然图像的反应.
- 在预测神经元对分布外 (OOD) 刺激的反应方面存在一个概括差距.
研究的目的:
- 研究DNN改进如何影响神经预测能力中的概括差距.
- 评估DNN架构,学习算法和数据集对这一差距的影响.
主要方法:
- 在对象识别通用化基准上评估DNN.
- 使用合成OOD刺激测试了神经预测能力.
- 分析了对象识别性能和神经预测性概括之间的关系.
主要成果:
- 在标准计算机视觉OOD基准上的表现没有预测OOD神经预测性.
- 对抗性强大的DNN显示了神经预测性概括的改进.
- 对抗强度的程度本身并不能预测神经预测性得分.
结论:
- 在当前的基准测试中改善DNN对象识别并不一定会增强灵长类动物视觉皮层的模型.
- 对抗性强度可能是改善神经预测概括性的更有希望的途径.
- 当前的基准标准可能无法完全捕捉到视觉处理概括的复杂性.
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