灵长类动物视觉系统和深度神经网络中的因子化视觉表示.
Jack W Lindsey1,2, Elias B Issa1,2
1Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.
eLife
|July 5, 2024
概括
分因,即场景信息的分离,是灵长类视觉系统和深度神经网络 (DNN) 的关键原则. 这一策略通过有效地组织视觉数据来增强对象识别.
科学领域:
- 神经科学是一个神经科学.
- 计算机视觉 计算机视觉
- 计算神经科学是一种神经科学.
背景情况:
- 对象分类是灵长类动物腹部视觉流的主要功能.
- 深度神经网络 (DNN) 模型被优化为视觉系统分类,但可能会丢弃或纠其他场景信息.
- 了解视觉区域如何代表物体身份之外的各种信息至关重要.
研究的目的:
- 研究因子化作为生物视觉表征中的规范原则.
- 为了确定场景参数的因子化是否能改善对象身份解码.
- 分析DNN中的因子分解,并将其与神经和行为数据进行比较.
主要方法:
- 在子腹视觉层次结构中,从对象身份中检查了对象姿势和背景的因子化.
- 评估了因数分解对对象身份解码性能的贡献.
- 在DNN模型库中分析了场景参数 (照明,背景,视角,姿势) 的分解.
- 将DNN性能与12个数据集中的人类和子的神经,fMRI和行为数据进行比较.
主要成果:
- 对象姿势和背景的因子化从身份增加在子更高的视觉区域.
- 增加的因子化显著改善了生物视觉中的对象身份解码.
- 最好匹配神经和行为数据的DNN模型表现出更强的场景参数因子化.
- 与场景参数的不变性与相应的生物数据相比,与因子化相比,与相应的生物数据的相关性不太一致.
结论:
- 分因式是生物视觉表征中的规范原则.
- 视觉场景信息的因子化是大脑和DNN中普遍存在的策略.
- 在因子化子空间中保持非类信息通常比抛弃它 (不变性) 更好.
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