DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery

TL;DR

This paper introduces Denoised Neighborhood Aggregation (DNA), a self-supervised method that improves fine-grained category discovery by leveraging semantic neighbor information and filtering noise to produce more compact and accurate clusters.

Contribution

The paper proposes a novel self-supervised framework that encodes semantic structures into embeddings and filters noisy neighbors, with theoretical justification linking it to clustering loss.

Findings

21.31% accuracy improvement in neighbor retrieval

Outperforms state-of-the-art models by 9.96% on three metrics

Effective in discovering fine-grained categories from coarse labels

Abstract

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 https://github.com/Lackel/DNA.