Upcoming Lectures
Upcoming Lectures
Lecture 11 Brain-inspired machine learning methods
受脑认知功能启发的机器学习方法
日期: 2020-05-22 点击：
Abstract
In this talk, I will present four novel objective functions that are inspired by human brain visual cognitive mechanisms, and are effective for training better Convolutional Nueral Networks (CNN). The Min-Max objective function is inspired by the manifold separation property of human visual cortex, which enforces the CNN model to learn features with minimized within-class distances and maximized between-class distances. These proposed objective functions are independent of, and can be applied to any CNN models. Comprehensive performance evaluations show remarkable performance improvements of the representative CNN models on the respective tasks without increasing their model complexities.
本课题组通过与脑认知科学领域的专家学者进行深层次的合作，从人脑视觉感知与认知机理的研究中获取灵感，并致力于将人脑视觉通道的某些特性转化为计算模型，在不改变现有神经网络层数及复杂度的情况下显著提升其图像识别精度。本次报告将概括介绍本课题组在这方面的若干最新研究成果，针对当前深度学习卷积神经网络的研究提出新的研究思路与方向。
Speaker Bio
YIHONG GONG is a distinguished professor, the dean of School of Software Engineering of Xi’an Jiaotong University, an IEEE Fellow, and a vice director of the National Engineering Laboratory for Visual Information Processing. His research interests include image/video content analysis and machine learning algorithms. He is among the first batch of researchers in the world initiating research studies on content-based image retrieval, sports video event detection, text/video content summarization, and image classification using the sparse coding image features.
龚怡宏教授目前是西安交通大学软件学院院长，国家特聘教授，IEEE Fellow，国家973项目首席科学家，视觉信息处理国家工程实验室副主任，陕西省人工智能联合实验室执行副主任。研究领域包括人工智能，计算机视觉及多媒体内容分析等。是学术界最早开展体育视频内容分析，图像内容检索，以及提出图像稀疏编码特征向量的国际知名学者。
