This project includes our research on deep neural networks and beyond. The topics are broad, including but not limited to neural architecture search , visual concepts , adversarial examples and defense , neural architecture design , object detection, deep forest, and few-shot and large-scale image recognition. The goal of the project is to develop interpretable and effective algorithms and systems for various computer vision tasks.
 Lingxi Xie, Alan Yuille, Genetic CNN, ICCV 2017
 Jianyu Wang, Zhishuai Zhang, Cihang Xie, Vittal Premachandran, Alan Yuille, Unsupervised learning of object semantic parts from internal states of CNNs by population encoding
 Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, Alan Yuille, Adversarial Examples for Semantic Segmentation and Object Detection, ICCV 2017
 Yan Wang, Lingxi Xie, Chenxi Liu, Siyuan Qiao, Ya Zhang, Wenjun Zhang, Qi Tian, Alan Yuille, SORT: Second-Order Response Transform for Visual Recognition, ICCV 2017