  People usually believe that network pruning not only reduces the
computational cost of deep networks, but also prevents overfitting by
decreasing model capacity. However, our work surprisingly discovers that
network pruning sometimes even aggravates overfitting. We report an unexpected
sparse double descent phenomenon that, as we increase model sparsity via
network pruning, test performance first gets worse (due to overfitting), then
gets better (due to relieved overfitting), and gets worse at last (due to
forgetting useful information). While recent studies focused on the deep double
descent with respect to model overparameterization, they failed to recognize
that sparsity may also cause double descent. In this paper, we have three main
contributions. First, we report the novel sparse double descent phenomenon
through extensive experiments. Second, for this phenomenon, we propose a novel
learning distance interpretation that the curve of $\ell_{2}$ learning distance
of sparse models (from initialized parameters to final parameters) may
correlate with the sparse double descent curve well and reflect generalization
better than minima flatness. Third, in the context of sparse double descent, a
winning ticket in the lottery ticket hypothesis surprisingly may not always
win.

Machine Learning;Computer Vision and Pattern Recognition