Use of Generative Adversarial Networks (GAN) for Taphonomic Image Augmentation and Model Protocol for the Deep Learning Analysis of Bone Surface Modifications

Applied Sciences

卷:11

Gabriel Cifuentes-Alcobendas¹  Manuel Domínguez-Rodrigo¹  Enrique Baquedano¹  Ander Fernández-Jaúregui¹ 

[1] Institute of Evolution in Africa (IDEA), Alcalá University, Covarrubias 36, 28010 Madrid, Spain;

关键词: generative adversarial networks;    optimizer;    activation function;    neural networks;    computer vision;    taphonomy;   

DOI  :  10.3390/app11115237

来源: DOAJ

【 摘 要 】

Deep learning models are based on a combination of neural network architectures, optimization parameters and activation functions. All of them provide exponential combinations whose computational fitness is difficult to pinpoint. The intricate resemblance of the microscopic features that are found in bone surface modifications make their differentiation challenging, and determining a baseline combination of optimizers and activation functions for modeling seems necessary for computational economy. Here, we experiment with combinations of the most resolutive activation functions (relu, swish, and mish) and the most efficient optimizers (stochastic gradient descent (SGD) and Adam) for bone surface modification analysis. We show that despite a wide variability of outcomes, a baseline of relu–SGD is advised for raw bone surface modification data. For imbalanced samples, augmented datasets generated through generative adversarial networks are implemented, resulting in balanced accuracy and an inherent bias regarding mark replication. In summary, although baseline procedures are advised, these do not prevent to overcome Wolpert’s “no free lunch” theorem and extend it beyond model architectures.

【 授权许可】

Unknown