This paper presents a simple unsupervised visual representation learning
method with a pretext task of discriminating all images in a dataset using a
parametric, instance-level classifier. The overall framework is a replica of a
supervised classification model, where semantic classes (e.g., dog, bird, and
ship) are replaced by instance IDs. However, scaling up the classification task
from thousands of semantic labels to millions of instance labels brings
specific challenges including 1) the large-scale softmax computation; 2) the
slow convergence due to the infrequent visiting of instance samples; and 3) the
massive number of negative classes that can be noisy. This work presents
several novel techniques to handle these difficulties. First, we introduce a
hybrid parallel training framework to make large-scale training feasible.
Second, we present a raw-feature initialization mechanism for classification
weights, which we assume offers a contrastive prior for instance discrimination
and can clearly speed up converge in our experiments. Finally, we propose to
smooth the labels of a few hardest classes to avoid optimizing over very
similar negative pairs. While being conceptually simple, our framework achieves
competitive or superior performance compared to state-of-the-art unsupervised
approaches, i.e., SimCLR, MoCoV2, and PIC under ImageNet linear evaluation
protocol and on several downstream visual tasks, verifying that full instance
classification is a strong pretraining technique for many semantic visual
tasks.

该研究提出了一种简单的无监督视觉表示学习方法，使用参数化的实例级分类器来区分数据集中的所有图像作为前提任务。这种方法解决了大规模训练的困难，并提供了一种相似度先验，以在多个视觉任务中实现了竞争性的性能表现。

为无监督视觉表示学习训练一百万类别的实例分类器

Train a One-Million-Way Instance Classifier for Unsupervised Visual  Representation Learning

In this paper, we leverage generative adversarial networks (GANs) to derive
an effective algorithm LLP-GAN for learning from label proportions (LLP), where
only the bag-level proportional information in labels is available. Endowed
with end-to-end structure, LLP-GAN performs approximation in the light of an
adversarial learning mechanism, without imposing restricted assumptions on
distribution. Accordingly, we can directly induce the final instance-level
classifier upon the discriminator. Under mild assumptions, we give the explicit
generative representation and prove the global optimality for LLP-GAN.
Additionally, compared with existing methods, our work empowers LLP solver with
capable scalability inheriting from deep models. Several experiments on
benchmark datasets demonstrate vivid advantages of the proposed approach.

使用生成对抗网络（GAN）进行学习，并通过建立具有逐步结构的 LLP-GAN 算法来学习标签比例，进而在不对分布施加限制的情况下得出最终的实例级分类器。该模型比现有方法更具可扩展性，这一点可以通过对基准数据集的实验得出结论。