The primary objective of this work is to present an alternative approach
aimed at reducing the dependency on labeled data. Our proposed method involves
utilizing autoencoder pre-training within a face image recognition task with
two step processes. Initially, an autoencoder is trained in an unsupervised
manner using a substantial amount of unlabeled training dataset. Subsequently,
a deep learning model is trained with initialized parameters from the
pre-trained autoencoder. This deep learning training process is conducted in a
supervised manner, employing relatively limited labeled training dataset.
During evaluation phase, face image embeddings is generated as the output of
deep neural network layer. Our training is executed on the CelebA dataset,
while evaluation is performed using benchmark face recognition datasets such as
Labeled Faces in the Wild (LFW) and YouTube Faces (YTF). Experimental results
demonstrate that by initializing the deep neural network with pre-trained
autoencoder parameters achieve comparable results to state-of-the-art methods.

研究论文旨在提出一种减少对标记数据依赖的替代方法，通过在面部图像识别任务中利用自编码器预训练和两步过程来实现。自编码器首先以无监督的方式使用大量无标签的训练数据集进行训练，然后使用预训练自编码器的初始化参数进行深度学习模型的训练。该方法的实验结果表明，使用预训练自编码器参数初始化的深度神经网络可以达到与现有方法相媲美的效果。