The memorization effect of deep neural network (DNN) plays a pivotal role in many state-of-the-art label-noise learning methods. To exploit this property, the early stopping trick, which stops the optimization at the early stage of training, is usually adopted. Current methods generally decide the early stopping point by considering a DNN as a whole. However, a DNN can be considered as a composition of a series of layers, and we find that the latter layers in a DNN are much more sensitive to label noise, while their former counterparts are quite robust. Therefore, selecting a stopping point for the whole network may make different DNN layers antagonistically affected each other, thus degrading the final performance. In this paper, we propose to separate a DNN into different parts and progressively train them to address this problem. Instead of the early stopping, which trains a whole DNN all at once, we initially train former DNN layers by optimizing the DNN with a relatively large number of epochs. During training, we progressively train the latter DNN layers by using a smaller number of epochs with the preceding layers fixed to counteract the impact of noisy labels. We term the proposed method as progressive early stopping (PES). Despite its simplicity, compared with the early stopping, PES can help to obtain more promising and stable results. Furthermore, by combining PES with existing approaches on noisy label training, we achieve state-of-the-art performance on image classification benchmarks.
翻译:深层神经网络(DNN)的记忆作用在许多最先进的标签-噪音学习方法中发挥着关键作用。 因此,为整个网络选择一个停止点可能会使不同的 DNN 层彼此产生对抗性的影响,从而降低最后的绩效。 在本文中,我们提议将DNN 分成不同的部分,并逐步培训它们来解决这一问题。而不是提前停止,我们一度将整个 DNN 训练成一个完整的DNN 层,我们最初通过优化DNN 层来培训以前的DNN 层,方法是以相对较大的数量来优化DNN 层。在培训过程中,我们逐步培训后DNND层,方法是利用较少的分层来相互对立,从而降低DNND层的相互影响,从而降低最后的绩效。我们建议将DNNN分成不同的部分,并逐步培训它们来解决这个问题。 早期,我们用更稳定的PESBM 来遏制其效果,我们用更好的方法,通过早期的升级的方式,通过升级的方式来抑制它的影响。