Discovering fine-grained categories from coarsely labeled data is a practical
and challenging task, which can bridge the gap between the demand for
fine-grained analysis and the high annotation cost. Previous works mainly focus
on instance-level discrimination to learn low-level features, but ignore
semantic similarities between data, which may prevent these models learning
compact cluster representations. In this paper, we propose Denoised
Neighborhood Aggregation (DNA), a self-supervised framework that encodes
semantic structures of data into the embedding space. Specifically, we retrieve
k-nearest neighbors of a query as its positive keys to capture semantic
similarities between data and then aggregate information from the neighbors to
learn compact cluster representations, which can make fine-grained categories
more separatable. However, the retrieved neighbors can be noisy and contain
many false-positive keys, which can degrade the quality of learned embeddings.
To cope with this challenge, we propose three principles to filter out these
false neighbors for better representation learning. Furthermore, we
theoretically justify that the learning objective of our framework is
equivalent to a clustering loss, which can capture semantic similarities
between data to form compact fine-grained clusters. Extensive experiments on
three benchmark datasets show that our method can retrieve more accurate
neighbors (21.31% accuracy improvement) and outperform state-of-the-art models
by a large margin (average 9.96% improvement on three metrics). Our code and
data are available at this https URL

从粗略标记的数据中发现细粒度类别是一项实用且具有挑战性的任务，本文提出了一种自我监督的框架 ——Denoised Neighborhood Aggregation (DNA)，它能够将数据的语义结构编码到嵌入空间中，通过聚合邻居信息来学习紧凑的聚类表示，并能够捕捉到数据之间的语义相似性和形成紧凑的细粒度聚类。