论文标题
基于暹罗网络和合成数据的新型转移学习方案
Novel transfer learning schemes based on Siamese networks and synthetic data
论文作者
论文摘要
基于经过大量图像Corpora培训的深网的转移学习方案提供了计算机视觉的最先进技术。在这里，有监督和半监督的方法构成了有效的技术，这些技术与相对小的数据集配合得很好。但是，此类应用程序目前仅限于适当可用的DeepNetwork模型的应用程序域。在这项贡献中，我们解决了生物技术领域中的一个重要应用领域，对微流体单细胞培养中CHO-K1悬浮量增长的自动分析，在该培养中，数据特征与现有领域非常不同，并且训练有素的深网无法轻松地通过古典转移学习来调整。我们提出了一种新颖的转移学习方案，该方案扩展了最近引入的双Vae体系结构，该结构对现实和合成数据进行了培训，我们将其专门的培训程序修改为转移学习域。在特定领域，通常只有很少的标签，注释昂贵。我们研究了一种新颖的转移学习策略，该策略将使用不变的共享表示以及合适的目标变量进行了自然和合成数据的同时进行重新培训，同时它将学会地处理不同显微镜技术的看不见数据。我们展示了双胞胎体系结构在图像处理中的最先进传递学习方法以及经典图像处理技术的优势，即使训练时间大大缩短并导致了在该域中的令人满意的结果，这些方法仍然存在。源代码可在https://github.com/dstallmann/transfer_learning_twinvae（works cross-platform）上获得开放源和免费（MIT许可）软件。我们在https://pub.uni-bielefeld.de/record/2960030中提供数据集。
Transfer learning schemes based on deep networks which have been trained on huge image corpora offer state-of-the-art technologies in computer vision. Here, supervised and semi-supervised approaches constitute efficient technologies which work well with comparably small data sets. Yet, such applications are currently restricted to application domains where suitable deepnetwork models are readily available. In this contribution, we address an important application area in the domain of biotechnology, the automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation, where data characteristics are very dissimilar to existing domains and trained deep networks cannot easily be adapted by classical transfer learning. We propose a novel transfer learning scheme which expands a recently introduced Twin-VAE architecture, which is trained on realistic and synthetic data, and we modify its specialized training procedure to the transfer learning domain. In the specific domain, often only few to no labels exist and annotations are costly. We investigate a novel transfer learning strategy, which incorporates a simultaneous retraining on natural and synthetic data using an invariant shared representation as well as suitable target variables, while it learns to handle unseen data from a different microscopy tech nology. We show the superiority of the variation of our Twin-VAE architecture over the state-of-the-art transfer learning methodology in image processing as well as classical image processing technologies, which persists, even with strongly shortened training times and leads to satisfactory results in this domain. The source code is available at https://github.com/dstallmann/transfer_learning_twinvae, works cross-platform, is open-source and free (MIT licensed) software. We make the data sets available at https://pub.uni-bielefeld.de/record/2960030.
