Temporary feature collapse phenomenon in early learning of MLPs

Dongrui Liu, Shaobo Wang, Jie Ren, Kangrui Wang, Sheng Yin, Huiqi Deng, Quanshi Zhang

Keywords: Neural Networks, Deep Learning Theory, Multi-Layer Perceptrons

TL;DR: In this paper, we focus on a typical two-phase phenomenon in the learning of multi-layer perceptrons (MLPs), and we discover and explain the reason for the feature collapse in the first phase.

Abstract: In this paper, we focus on a typical two-phase phenomenon in the learning of multi-layer perceptrons (MLPs). We discover and explain the reason for the feature collapse phenomenon in the first phase, i.e., the diversity of features over different samples keeps decreasing in the first phase, until samples of different categories share almost the same feature, which hurts the optimization of MLPs. We explain such a phenomenon in terms of the learning dynamics of MLPs. Furthermore, we theoretically analyze the reason why four typical operations can alleviate the feature collapse. The code has been attached with the submission.

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