论文标题
深度表示学习的无监督图像分类
Unsupervised Image Classification for Deep Representation Learning
论文作者
论文摘要
反对自我监督学习的深度聚类是无监督的视觉表示学习的一个非常重要且有希望的方向，因为它几乎不需要域知识来设计借口任务。但是，嵌入聚类的关键组件将其扩展限制在非常大规模的数据集上，因为其先决条件是保存整个数据集的全局潜在嵌入。在这项工作中，我们旨在使该框架更简单，优雅，而不会下降。我们在不使用嵌入聚类的情况下提出了一个无监督的图像分类框架，该框架与标准监督训练方式非常相似。为了详细的解释，我们通过深入的聚类和对比度学习进一步分析了其关系。已经对Imagenet数据集进行了广泛的实验，以证明我们方法的有效性。此外，有关转移学习基准测试基准的实验已验证其对其他下游任务的概括，包括多标签图像分类，对象检测，语义分割和少量图像分类。
Deep clustering against self-supervised learning is a very important and promising direction for unsupervised visual representation learning since it requires little domain knowledge to design pretext tasks. However, the key component, embedding clustering, limits its extension to the extremely large-scale dataset due to its prerequisite to save the global latent embedding of the entire dataset. In this work, we aim to make this framework more simple and elegant without performance decline. We propose an unsupervised image classification framework without using embedding clustering, which is very similar to standard supervised training manner. For detailed interpretation, we further analyze its relation with deep clustering and contrastive learning. Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method. Furthermore, the experiments on transfer learning benchmarks have verified its generalization to other downstream tasks, including multi-label image classification, object detection, semantic segmentation and few-shot image classification.
