A Transfer Learning-Based Multi-Instance Learning Method With Weak Labels

By Yanshan Xiao, Fei-Yao Liang, Bo Liu. 2020

31 citations recorded by the source corpus.

In multi-instance learning (MIL), labels are associated with bags rather than the instances in the bag. Most of the previous MIL methods assume that each bag has the actual label in the training set. However, from the process of labeling work, the label of a bag is always evaluated by the calculation of the labels obtained from a number of labelers. In the calculation, the weight of each labeler is always unknown and people always assign the weight for each labeler by random or equally, and this may result in the ambiguous labels for the bags, which is called weak labels here. In addition, we always meet the problem of knowledge transfer from the source task to the target task, and this leads to the study of multiple instance transfer learning. In this article, we propose a new framework called transfer learning-based multiple instance learning (TMIL) framework to address the problem of multiple instance transfer learning in which both the source task and the target task contain the weak labels. We first construct a TMIL model with weak labels, which can transfer knowledge from the source task to the target task where both source and target tasks contain weak labels. We then put forward an iterative framework to solve the transfer learning model with weak labels so that we can update the label of the bag to improve the performance of multiple instance learning. We then present the convergence analysis of the proposed method. The experiments show that the proposed method outperforms the existing multiple instance learning methods and can correct the initial labels to obtain the actual labels for the bags.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dacheng Tao
2. Shihao Bai
3. Aishan Liu
4. Yuqing Ma
5. Xianglong Liu
6. Jing Zhang
7. Charu C. Aggarwal
8. Jun Yu

In Computer Vision and Pattern Recognition

1. Xianglong Liu
2. Dacheng Tao
3. Shihao Bai
4. Aishan Liu
5. Edwin R. Hancock
6. Yuqing Ma
7. Ling Shao
8. Luc Van Gool

In Representation & Self-Supervised Learning

1. Dacheng Tao
2. Xianglong Liu

Citations in the corpus

• Regionwise Generative Adversarial Image Inpainting for Large Missing Areas (2019, background): “The development of deep neural networks [22], [23] has significantly accelerated the progress of computer vision tasks [24], [25].”