20161026-36 周博磊：Understanding and Leveraging the Internal Representation of. ...

报告嘉宾1：周博磊 （ MIT ）

报告时间：2016年10月26日（星期三）晚20:00（北京时间）

报告题目：Understanding and Leveraging the Internal Representation of Convolutional Neural Networks.

主持人：苏航 （清华大学）

With the success of deep learning architectures such as convolutional neural networks (CNN) for visual processing and the access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. One important factor for continued progress is to understand the representations that are learned by the inner layers of these deep learning architectures. Here we have a comparison study to analyze the representations of the CNN trained on ImageNet for object recognition and the CNN trained on Places Database for scene recognition respectively. As scenes are composed of objects, the CNN for scene classification automatically learns to discover meaningful objects detectors. With object detectors emerging as a result of learning to recognize scenes, our work demonstrates that the same network can perform both scene recognition and object localization in a single forward-pass, without ever having been explicitly taught the notion and localization of the objects. The presentation is based on my work published in ICLR'15 and CVPR'16.

Bolei Zhou is the 4th-year PhD student in Computer Science and Artificial Intelligence Laboratory at MIT, advised by Prof. Antonio Torralba. His research interest is on computer vision and machine learning. His research interest is on deep learning and high-level vision & AI tasks such as scene understanding and visual recognition. He is the award recipient of Facebook Fellowship and Microsoft Research Asia Fellowship.

VOOC责任委员：任传贤（中山大学）

VODB协调理事：张利军（南京大学），章国锋（浙江大学）