The recent advances in representation learning inspire us to take on the
challenging problem of unsupervised image classification tasks in a principled
way. We propose ContraCluster, an unsupervised image classification method that
combines clustering with the power of contrastive self-supervised learning.
ContraCluster consists of three stages: (1) contrastive self-supervised
pre-training (CPT), (2) contrastive prototype sampling (CPS), and (3)
prototype-based semi-supervised fine-tuning (PB-SFT). CPS can select highly
accurate, categorically prototypical images in an embedding space learned by
contrastive learning. We use sampled prototypes as noisy labeled data to
perform semi-supervised fine-tuning (PB-SFT), leveraging small prototypes and
large unlabeled data to further enhance the accuracy. We demonstrate
empirically that ContraCluster achieves new state-of-the-art results for
standard benchmark datasets including CIFAR-10, STL-10, and ImageNet-10. For
example, ContraCluster achieves about 90.8% accuracy for CIFAR-10, which
outperforms DAC (52.2%), IIC (61.7%), and SCAN (87.6%) by a large margin.
Without any labels, ContraCluster can achieve a 90.8% accuracy that is
comparable to 95.8% by the best supervised counterpart.

使用对比自监督学习结合聚类的方法，提出了一种新的半监督图像分类方法 ContraCluster，通过三个阶段的学习可选取出高精度的图像原型并再结合少量的无标签数据和原型的噪声标签进行半监督训练，成果达到了与有监督分类相当的高准确率。

ContraCluster: 利用对比度自我监督学习和基于原型的半监督学习实现无标签分类

ContraCluster: Learning to Classify without Labels by Contrastive  Self-Supervision and Prototype-Based Semi-Supervision

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.

本研究提出了一种无监督图像分类框架，旨在通过不使用嵌入聚类来简化和优化深度聚类算法，并在 ImageNet 数据集上进行了实验。同时验证了其在多标签图像分类，目标检测，语义分割等任务的泛化性，以及其在迁移学习中的有效性。

深度表示学习的无监督图像分类

Unsupervised Image Classification for Deep Representation Learning

Can we automatically group images into semantically meaningful clusters when
ground-truth annotations are absent? The task of unsupervised image
classification remains an important, and open challenge in computer vision.
Several recent approaches have tried to tackle this problem in an end-to-end
fashion. In this paper, we deviate from recent works, and advocate a two-step
approach where feature learning and clustering are decoupled. First, a
self-supervised task from representation learning is employed to obtain
semantically meaningful features. Second, we use the obtained features as a
prior in a learnable clustering approach. In doing so, we remove the ability
for cluster learning to depend on low-level features, which is present in
current end-to-end learning approaches. Experimental evaluation shows that we
outperform state-of-the-art methods by large margins, in particular +26.6% on
CIFAR10, +25.0% on CIFAR100-20 and +21.3% on STL10 in terms of classification
accuracy. Furthermore, our method is the first to perform well on a large-scale
dataset for image classification. In particular, we obtain promising results on
ImageNet, and outperform several semi-supervised learning methods in the
low-data regime without the use of any ground-truth annotations. The code is
made publicly available at
this https URL

本文提出一种两步法的图像无监督分类方法，通过自我监督学习获取具有语义意义的特征，然后将其作为可学习聚类方法的先验，达到优化图像聚类的效果。实验证明，该方法大幅优于现有最先进方法，并在大规模图像分类数据集 ImageNet 上取得了有希望的结果。