Self-Training of Graph Neural Networks using Similarity Reference for Robust Training with Noisy Labels

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 13
  • Download : 0
Filtering noisy labels is crucial for robust training of deep neural networks. To train networks with noisy labels, sampling methods have been introduced, which sample the reliable instances to update networks using only sampled data. Since they rarely employ the non-sampled data for training, these methods have a fundamental limitation that they reduce the amount of the training data. To alleviate this problem, our approach aims to fully utilize the whole dataset by leveraging the information of the sampled data. To this end, we propose a novel graph-based learning framework that enables networks to propagate the label information of the sampled data to adjacent data, whether they are sampled or not. Also, we propose a novel self-training strategy to utilize the non-sampled data without labels and to regularize the network update using the information of the sampled data. Our method outperforms state-of-the-art sampling methods.
Publisher
IEEE Signal Processing Society
Issue Date
2020-10-26
Language
English
Citation

2020 IEEE International Conference on Image Processing (ICIP)

DOI
10.1109/ICIP40778.2020.9191054
URI
http://hdl.handle.net/10203/278683
Appears in Collection
EE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0