Quantum convolutional neural network (QCNN), an early application for quantum
computers in the NISQ era, has been consistently proven successful as a machine
learning (ML) algorithm for several tasks with significant accuracy. Derived
from its classical counterpart, QCNN is prone to overfitting. Overfitting is a
typical shortcoming of ML models that are trained too closely to the availed
training dataset and perform relatively poorly on unseen datasets for a similar
problem. In this work we study the adaptation of one of the most successful
overfitting mitigation method, knows as the (post-training) dropout method, to
the quantum setting. We find that a straightforward implementation of this
method in the quantum setting leads to a significant and undesirable
consequence: a substantial decrease in success probability of the QCNN. We
argue that this effect exposes the crucial role of entanglement in QCNNs and
the vulnerability of QCNNs to entanglement loss. To handle overfitting, we
proposed a softer version of the dropout method. We find that the proposed
method allows us to handle successfully overfitting in the test cases.

量子卷积神经网络 (QCNN) 是在 NISQ 时代中早期应用的量子计算机，已被证明在多个任务中具有显著准确性的机器学习 (ML) 算法。我们研究了将一种成功的过拟合缓解方法（称为（训练后）dropout 方法）转化为量子设置，并发现基于此方法的量子设置实现导致了 QCNN 成功概率的显著下降，揭示了量子卷积神经网络中纠缠的关键作用以及其对纠缠丧失的脆弱性。为了应对过拟合问题，我们提出了 dropout 方法的更加平滑的版本，并在测试案例中成功地处理了过拟合。

软装 dropout：量子卷积神经网络中减少过拟合的实用方法

Soft-Dropout: A Practical Approach for Mitigating Overfitting in Quantum  Convolutional Neural Networks

Deep Neural Networks (DNNs) are built using artificial neural networks. They
are part of machine learning methods that are capable of learning from data
that have been used in a wide range of applications. DNNs are mainly
handcrafted and they usually contain numerous layers. Research frontier has
emerged that concerns automated construction of DNNs via evolutionary
algorithms. This paper emphasizes the importance of what we call
two-dimensional brain evolution and how it can inspire two dimensional DNN
evolutionary modeling. We also highlight the connection between the dropout
method which is widely-used in regularizing DNNs and neurogenesis of the brain,
and how these concepts could benefit DNNs evolution.The paper concludes with
several recommendations for enhancing the automatic construction of DNNs.

本文强调了人脑的二维进化对于深度神经网络的进化建模的重要性，提出了通过进化算法对深度神经网络进行自动构建的研究前沿，并探讨了 dropout 方法和神经发生与 DNNs 进化之间的联系，为增强深度神经网络的自动构建提出了几项建议。

神经发生、神经达尔文主义和物种进化是否都可以成为深度神经网络演化的灵感来源？

Is it conceivable that neurogenesis, neural Darwinism, and species  evolution could all serve as inspiration for the creation of evolutionary  deep neural networks?

In this paper, we propose a novel 3D human pose estimation algorithm from a
single image based on neural networks. We adopted the structure of the
relational networks in order to capture the relations among different body
parts. In our method, each pair of different body parts generates features, and
the average of the features from all the pairs are used for 3D pose estimation.
In addition, we propose a dropout method that can be used in relational
modules, which inherently imposes robustness to the occlusions. The proposed
network achieves state-of-the-art performance for 3D pose estimation in Human
3.6M dataset, and it effectively produces plausible results even in the
existence of missing joints.

本文提出了一种基于神经网络的新型三维人体姿态估计算法，采用关系网络的结构来捕捉不同身体部位之间的关系，并使用每对不同身体部位生成的特征的平均值进行 3D 姿态估计，提出的网络在 Human 3.6M 数据集中实现了最先进的性能，并能在缺失关节的情况下有效地产生可信的结果。