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Deep nets do very well on specific types of visual tasks and on specific benchmarked datasets. For Cognitive Science, Deep Nets offer the possibility of developing computational theories which can be tested on natural, or realistically synthetic images. Many topics are covered in this project, including but not limited to compare the performance of various Deep Nets models with humans (or primates), design computational algorithms that exhibit the robustness of biological vision [1,2], vision and text analogy, analysis by synthesis [3] etc. We are cooperating with other groups in JHU and MIT.

[1] Jianyu Wang, Cihang Xie, Zhishuai Zhang, Jun Zhu, Lingxi Xie, Alan Yuille, Detecting Semantic Parts on Partially Occluded Objects, BMVC 2017
[2] Boyang Deng, Qing Liu, Siyuan Qiao, Alan Yuille, Few-shot Learning by Exploiting Visual Concepts within CNNs
[3] Alan Yuille, Daniel Kersten, Vision as Bayesian Inference: Analysis by Synthesis? In Trends in Cognitive Neuroscience 2006