Finding the optimal size of deep learning models is very actual and of broad impact, especially in energy-saving schemes. Very recently, an unexpected phenomenon, the ``double descent'', has caught the attention of the deep learning community. As the model's size grows, the performance gets first worse, and then goes back to improving. It raises serious questions about the optimal model's size to maintain high generalization: the model needs to be sufficiently over-parametrized, but adding too many parameters wastes training resources. Is it possible to find, in an efficient way, the best trade-off? Our work shows that the double descent phenomenon is potentially avoidable with proper conditioning of the learning problem, but a final answer is yet to be found. We empirically observe that there is hope to dodge the double descent in complex scenarios with proper regularization, as a simple $\ell_2$ regularization is already positively contributing to such a perspective.

在深度学习模型中，找到最佳大小对于节能取得高广泛影响。 通过恰当的条件，可能避免双下降现象，其中模型大小增长时性能会先变差然后变好，以维持高泛化的效果需要充分过参数化的模型，但添加太多参数会浪费培训资源。在复杂情况下，正则化已经对避免双下降产生了积极作用。

深度神经网络是否能避免双重下降现象？