Abstract
Unsupervised learning has grown in popularity because of the difficulty of collecting annotated data and the development of modern frameworks that allow us to learn from unlabeled data. Existing studies, however, either disregard variations at different levels of similarity or only consider negative samples from one batch. We argue that image pairs should have varying degrees of similarity, and the negative samples should be allowed to be drawn from the entire dataset. In this work, we propose Search-based Unsupervised Visual Representation Learning (SUVR) to learn better image representations in an unsupervised manner. We first construct a graph from the image dataset by the similarity between images, and adopt the concept of graph traversal to explore positive samples. In the meantime, we make sure that negative samples can be drawn from the full dataset. Quantitative experiments on five benchmark image classification datasets demonstrate that SUVR can significantly outperform strong competing methods on unsupervised embedding learning. Qualitative experiments also show that SUVR can produce better representations in which similar images are clustered closer together than unrelated images in the latent space.
Abstract (translated)
非监督学习因其收集标注数据的困难以及现代框架的发展而变得越来越受欢迎。然而，现有的研究要么忽略了不同相似性水平下的变化，要么只考虑了一组样本中的消极样本。我们认为图像对应该具有不同程度的相似性，并且消极样本应该从整个数据集随机抽取。在本研究中，我们提出了基于搜索的非监督视觉表示学习(SUVR)，以在没有监督嵌入学习的情况下学习更好的图像表示。我们首先通过图像之间的相似性构建图像集的图，并采用图遍历的概念来探索积极样本。同时，我们确保可以从整个数据集随机抽取消极样本。对五个基准图像分类数据集进行定量实验表明，SUVR可以在无监督嵌入学习中显著优于强大的竞争方法。定性实验也表明，SUVR可以在潜在空间中相似的图像簇在一起，比无关的图像在分离空间中更紧密地聚集在一起，从而生成更好的表示。
URL
https://arxiv.org/abs/2305.14754
