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日期：2020-10-09
来源：
题目：Can Deep Learning Learn to Count?—on cognitive deficit of the current state of deep learning
主讲：武筱林 教授
时间：10月15日 8：30
地点：科创中心A栋512
主办：人工智能产业技术研究院
专家简介：
武筱林，国家“千人计划”创新人才，麦克马斯特大学电子与计算机工程系教授，上海交通大学电子信息与电气工程学院教授，博导。其研究领域涵盖多媒体（尤其是视觉信号）计算和通信、图像处理、信号量化理论、多媒体数据压缩、联合信源与信道编码等。武筱林教授是IEEE Fellow，国际图像处理界顶级学术刊物IEEE Transaction on Image Processing副主编，还曾任IEEE Transaction on Multimedia副主编，历届IEEE国际图像处理年会技术委员会成员和IEEE数字多媒体年会技术委员会成员。
报告主要内容：
Subitizing, or the sense of small natural numbers, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills, language or arithmetic. Given successes of deep learning (DL) in tasks of visual intelligence and given the primitivity of number sense, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the connectionist CNN machinery itself. A recurrent neural network capable of subitizing does exist, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also, we investigate, using subitizing as a test bed, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting, pointing to both cognitive deficit of pure DL, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful as visual numerosity represents a minimum level of human intelligence.
