Learning with Noisy Labels for Human Fall Events Classification: Joint
  Cooperative Training with Trinity Networks
Leiyu Xie Yang Sun Syed Mohsen Naqvi
With the increasing ageing population, fall events classification has drawn
much research attention. In the development of deep learning, the quality of
data labels is crucial. Most of the datasets are labelled automatically or
semi-automatically, and the samples may be mislabeled, which constrains the
performance of Deep Neural Networks (DNNs). Recent research on noisy label
learning confirms that neural networks first focus on the clean and simple
instances and then follow the noisy and hard instances in the training stage.
To address the learning with noisy label problem and protect the human
subjects' privacy, we propose a simple but effective approach named Joint
Cooperative training with Trinity Networks (JoCoT). To mitigate the privacy
issue, human skeleton data are used. The robustness and performance of the
noisy label learning framework is improved by using the two teacher modules and
one student module in the proposed JoCoT. To mitigate the incorrect selections,
the predictions from the teacher modules are applied with the consensus-based
method to guide the student module training. The performance evaluation on the
widely used UP-Fall dataset and comparison with the state-of-the-art, confirms
the effectiveness of the proposed JoCoT in high noise rates. Precisely, JoCoT
outperforms the state-of-the-art by 5.17% and 3.35% with the averaged pairflip
and symmetric noises, respectively.