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This project includes our research on deep neural networks and beyond. The topics are broad, including but not limited to neural architecture search [1], visual concepts [2], adversarial examples and defense [3], neural architecture design [4], 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.

[1] Lingxi Xie, Alan Yuille, Genetic CNN, ICCV 2017
[2] Jianyu Wang, Zhishuai Zhang, Cihang Xie, Vittal Premachandran, Alan Yuille, Unsupervised learning of object semantic parts from internal states of CNNs by population encoding
[3] Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, Alan Yuille, Adversarial Examples for Semantic Segmentation and Object Detection, ICCV 2017
[4] 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