VALSE 论文速览 第133期：基于自监督对抗噪声掩码的噪声标签学习 ...

论文题目：Learning with Noisy labels via Self-supervised Adversarial Noisy Masking

作者列表：

涂远鹏 (同济大学)、张博深 (腾讯优图)、李昱希 (腾讯优图)、刘亮 (腾讯优图)、李剑 (腾讯优图)、张江宁 (腾讯优图)、王亚彪 (腾讯优图)、汪铖杰 (腾讯优图)、赵才荣 (同济大学)

B站观看网址：

https://www.bilibili.com/video/BV17w411r7SP/

论文摘要：

Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mislabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of models. The proposed method is simple yet effective, it is tested on synthetic and real-world noisy datasets, where significant improvements are obtained over previous methods.

论文信息：

[1] Yuanpeng Tu , Boshen Zhang, Yuxi Li, Liang Liu, Jian Li, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Cai Rong Zhao, “Learning with Noisy labels via Self-supervised Adversarial Noisy Masking,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR2023), Vancouver Convention Center, Jun, 2023.

论文链接：

[https://arxiv.org/abs/2302.06805]

代码链接：

[https://github.com/yuanpengtu/SANM]

视频讲者简介：

涂远鹏，同济大学电子与信息工程学院硕士生。主要研究为行人再识别与噪声标签学习。

个人主页：

https://yuanpengtu.github.io/

同济大学视觉与智能学习实验室主页：

https://vill.tongji.edu.cn/

特别鸣谢本次论文速览主要组织者：

月度轮值AC：叶茫 (武汉大学)