This paper presents a novel method for autonomously enhancing deep neural
network training. My approach employs an Evaluation Neural Network (ENN)
trained via deep reinforcement learning to predict the performance of the
target network. The ENN then works as an additional evaluation function during
backpropagation. Computational experiments with Multi-Layer Perceptrons (MLPs)
demonstrate the method's effectiveness. By processing input data at 0.15^2
times its original resolution, the ENNs facilitated efficient inference.
Results indicate that MLPs trained with the proposed method achieved a mean
test accuracy of 93.02%, which is 2.8% higher than those trained solely with
conventional backpropagation or with L1 regularization. The proposed method's
test accuracy is comparable to networks initialized with He initialization
while reducing the difference between test and training errors. These
improvements are achieved without increasing the number of epochs, thus
avoiding the risk of overfitting. Additionally, the proposed method dynamically
adjusts gradient magnitudes according to the training stage. The optimal ENN
for enhancing MLPs can be predicted, reducing the time spent exploring optimal
training methodologies. The explainability of ENNs is also analyzed using
Grad-CAM, demonstrating their ability to visualize evaluation bases and
supporting the Strong Lottery Ticket hypothesis.

使用强化学习训练的评估神经网络（ENN）作为反向传播中的附加评估函数，提高深度神经网络训练的方法，通过对 0.15^2 倍分辨率的输入数据进行处理，ENN 实现有效的推理，结果表明，使用该方法训练的 MLPs 的平均测试准确率达到了 93.02%，比仅使用传统反向传播或 L1 正则化训练的模型高出 2.8%，而且测试准确率与 He 初始化网络相当，缩小了测试误差和训练误差之间的差距，并且无需增加训练轮数，避免过拟合风险，还能根据训练阶段动态调整梯度大小，降低了寻找最佳训练方法所需的时间，论文还使用 Grad-CAM 对 ENN 的可解释性进行了分析，展示了它们可视化评估基数的能力，并支持了强大的抽奖票假设。