We present SSL-HV: Self-Supervised Learning approaches applied to the task of
Handwriting Verification. This task involves determining whether a given pair
of handwritten images originate from the same or different writer distribution.
We have compared the performance of multiple generative, contrastive SSL
approaches against handcrafted feature extractors and supervised learning on
CEDAR AND dataset. We show that ResNet based Variational Auto-Encoder (VAE)
outperforms other generative approaches achieving 76.3% accuracy, while
ResNet-18 fine-tuned using Variance-Invariance-Covariance Regularization
(VICReg) outperforms other contrastive approaches achieving 78% accuracy. Using
a pre-trained VAE and VICReg for the downstream task of writer verification we
observed a relative improvement in accuracy of 6.7% and 9% over ResNet-18
supervised baseline with 10% writer labels.

我们研究了应用于手写验证任务上的自监督学习方法，对比了多种生成式对比式自监督学习方法和手工特征提取器以及有监督学习在 CEDAR AND 数据集上的表现。结果显示，基于 ResNet 的变分自动编码器（VAE）在准确度上超越其他生成式方法，达到 76.3% 的准确度；而使用方差不变协方差正则化（VICReg）进行微调的 ResNet-18 在准确度上超越其他对比式方法，达到 78% 的准确度。使用预训练的 VAE 和 VICReg 进行下游的写作者验证任务，相对于 ResNet-18 有监督基准模型和 10% 的写作者标签，准确度相对提高了 6.7% 和 9%。