SOSAE：自组织稀疏自动编码器
SOSAE: Self-Organizing Sparse AutoEncoder
AI总结：
针对自动编码器隐藏层大小调整耗资源的问题，提出自组织稀疏自动编码器（SOSAE），通过自组织正则化动态优化特征空间维度，实验显示其浮点运算量比基线少至多130倍且性能相当。
摘要：
调整自动编码器隐藏层大小的过程有助于为输入数据提供最优压缩表示。然而，以网格搜索为默认选项的超参数调整过程会耗费大量计算资源和时间。本文提出自动编码器的自组织正则化方法，可动态将特征空间维度调整至最优大小。受物理学概念启发，自组织稀疏自动编码器（SOSAE）以结构化方式在特征空间中引入稀疏性，允许截断特征向量的非活跃部分而不损失任何信息。这是通过基于特征向量维度的大小和位置索引对自动编码器进行惩罚实现的，训练过程中会从这两个方面约束特征空间。在多种数据集上的大量实验表明，SOSAE调整特征空间维度所需的浮点运算量（FLOPs）比其他基线方法少至多130倍，同时保持相同的调整质量和性能。
英文摘要：
The process of tuning the size of the hidden layers for autoencoders has the benefit of providing optimally compressed representations for the input data. However, such hyper-parameter tuning process would take a lot of computation and time effort with grid search as the default option. In this paper, we introduce the Self-Organization Regularization for Autoencoders that dynamically adapts the dimensionality of the feature space to the optimal size. Inspired by physics concepts, Self-Organizing Sparse AutoEncoder (SOSAE) induces sparsity in feature space in a structured way that permits the truncation of the non-active part of the feature vector without any loss of information. This is done by penalizing the autoencoder based on the magnitude and the positional index of the feature vector dimensions, which during training constricts the feature space in both terms. Extensive experiments on various datasets show that our SOSAE can tune the feature space dimensionality up to 130 times lesser Floating-point Operations (FLOPs) than other baselines while maintaining the same quality of tuning and performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
