论文标题
Slade：远程度量学习的自我训练框架
SLADE: A Self-Training Framework For Distance Metric Learning
论文作者
论文摘要
大多数现有的距离度量学习方法都使用完全标记的数据来学习嵌入空间中的样本相似性。我们提出一个自我训练框架Slade，以利用其他未标记的数据来提高检索性能。我们首先在标记的数据上训练教师模型，并使用它为未标记的数据生成伪标签。然后，我们在标签和伪标签上训练学生模型，以生成最终功能嵌入。我们使用自我监督的表示学习来初始化教师模型。为了更好地处理由教师网络生成的嘈杂的伪标签，我们为学生网络设计了一个新功能基础学习组件，该组件学习了未标记数据的功能表示的基础功能。学到的基础向量更好地测量了成对的相似性，并用于选择用于培训学生网络的高度自愿样本。我们评估了我们的标准检索基准测试方法：CUB-200，CARS-196和车内。实验结果表明，我们的方法显着提高了最先进方法的性能。
Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings. We use self-supervised representation learning to initialize the teacher model. To better deal with noisy pseudo labels generated by the teacher network, we design a new feature basis learning component for the student network, which learns basis functions of feature representations for unlabeled data. The learned basis vectors better measure the pairwise similarity and are used to select high-confident samples for training the student network. We evaluate our method on standard retrieval benchmarks: CUB-200, Cars-196 and In-shop. Experimental results demonstrate that our approach significantly improves the performance over the state-of-the-art methods.
