In this article, an adaptive learning framework is established for a deep weighted sparse autoencoder (AE) by resorting to the multiobjective evolutionary algorithm (MOEA). The weighted sparsity is introduced to facilitate the design of the varying degrees of the sparsity constraints imposed on the hidden units of the AE. The MOEA is exploited to adaptively seek appropriate hyperparameters, where the divide-and-conquer strategy is implemented to enhance the MOEA’s performance in the context of deep neural networks. Moreover, a sharing scheme is proposed to further reduce the time complexity of the learning process at the slight expense of the learning precision. It is shown via extensive experiments that the established adaptive learning framework is effective, where different sparse models are utilized to demonstrate the generality of the proposed results. Then, the generality of the proposed framework is examined on the convolutional AE and VGG-16 network. Finally, the developed framework is applied to the blind image quantity assessment that illustrates the applicability of the established algorithms.

中文翻译：

---

深度加权稀疏自动编码器的自适应学习框架：一种多目标进化算法


在本文中，利用多目标进化算法（MOEA）为深度加权稀疏自动编码器（AE）建立了自适应学习框架。引入加权稀疏性是为了方便设计对 AE 隐藏单元施加的不同程度的稀疏性约束。 MOEA 用于自适应地寻找合适的超参数，其中实施分而治之策略以增强 MOEA 在深度神经网络背景下的性能。此外，提出了一种共享方案，以稍微牺牲学习精度为代价，进一步降低学习过程的时间复杂度。通过大量的实验表明，所建立的自适应学习框架是有效的，其中利用不同的稀疏模型来证明所提出的结果的通用性。然后，在卷积 AE 和 VGG-16 网络上检查所提出框架的通用性。最后，将所开发的框架应用于盲图像质量评估，说明了所建立算法的适用性。