最近，马毅教授研究组在NeurIPS 2022上发表了一篇新论文，回顾了稀疏卷积模型在图像分类中的应用，并成功解决了稀疏卷积模型的经验性能和可解释性之间的不匹配问题。

11月15日（周二）19:00「青源Live 第53期丨戴锡笠：卷积稀疏编码在视觉识别任务中的探究」将在线举办，马毅教授研究组的戴锡笠将详细介绍这篇最新工作，欢迎预约观看并参与线上交流。

戴锡笠

香港科技大学（广州）博士在读

师从加州大学伯克利分校马毅教授，同时由沈向洋教授和倪明选教授指导。研究兴趣主要在闭环转录、3D视觉、稀疏编码和字典学习等检查方向，目前研究重点是通过闭环转录方法进行 3D 重建。

“

卷积稀疏编码在视觉识别任务中的探究

Despite strong empirical performance for image classification, deep neural networks are often regarded as ''black boxes'' and they are difficult to interpret. On the other hand, sparse convolutional models, which assume that a signal can be expressed by a linear combination of a few elements from a convolutional dictionary, are powerful tools for analyzing natural images with good theoretical interpretability and biological plausibility. However, such principled models have not demonstrated competitive performance when compared with empirically designed deep networks. This paper revisits the sparse convolutional modeling for image classification and bridges the gap between good empirical performance (of deep learning) and good interpretability (of sparse convolutional models). Our method uses differentiable optimization layers that are defined from convolutional sparse coding as drop-in replacements of standard convolutional layers in conventional deep neural networks. We show that such models have equally strong empirical performance on CIFAR-10, CIFAR-100, and ImageNet datasets when compared to conventional neural networks. By leveraging stable recovery property of sparse modeling, we further show that such models can be much more robust to input corruptions as well as adversarial perturbations in testing through a simple proper trade-off between sparse regularization and data reconstruction terms.

活动时间：11月15日（周二）19:00-20:00

活动形式：线上直播，扫描下方二维码报名

更多内容 尽在智源社区