In recent years transfer learning has attracted much attention due to its ability to adapt a well-trained model from one domain to another. Fine-tuning is one of the most widely-used methods which exploit a small set of labeled data in the target domain for adapting the network. Including a few methods using the labeled data in the source domain, most transfer learning methods require labeled datasets, and it restricts the use of transfer learning to new domains. In this paper, we propose a fully unsupervised self-tuning algorithm for learning visual features in different domains. The proposed method updates a pre-trained model by minimizing the triplet loss function using only unlabeled data in the target domain. First, we propose the relevance measure for unlabeled data by the bagged clustering method. Then triplets of the anchor, positive, and negative data points are sampled based on the ranking violations of the relevance scores and the Euclidean distances in the embedded feature space. This fully unsupervised self-tuning algorithm improves the performance of the network significantly. We extensively evaluate the proposed algorithm using various metrics, including classification accuracy, feature analysis, and clustering quality, on five benchmark datasets in different domains. Besides, we demonstrate that applying the self-tuning method on the fine-tuned network help achieve better results.

近年来，由于迁移学习能够将训练有素的模型从一个领域应用于另一个领域，因此备受关注。微调是最广泛使用的方法之一，它利用目标域中的一小组标记数据来适应网络。包括使用源域中标记数据的几种方法在内，大多数转移学习方法都需要标记数据集，并且将转移学习的使用限制在新域中。在本文中，我们提出了一种完全无监督的自调整算法，用于学习不同领域的视觉特征。所提出的方法通过仅使用目标域中未标记的数据来最小化三元组损失函数来更新预训练模型。首先，我们提出了袋装聚类方法对未标记数据的相关性度量。然后是三叉锚，正 根据相关分数的排名违规和嵌入式特征空间中的欧几里得距离对负数据点进行采样。这种完全不受监督的自调整算法可以显着提高网络性能。我们在不同领域的五个基准数据集上，使用各种指标（包括分类准确性，特征分析和聚类质量）广泛评估了该算法。此外，我们证明了在微调网络上应用自调整方法有助于获得更好的结果。我们在不同领域的五个基准数据集上，使用各种指标（包括分类准确性，特征分析和聚类质量）广泛评估了该算法。此外，我们证明了在微调网络上应用自调整方法有助于获得更好的结果。我们在不同领域的五个基准数据集上，使用各种指标（包括分类准确性，特征分析和聚类质量）广泛评估了该算法。此外，我们证明了在微调网络上应用自调整方法有助于获得更好的结果。