用布隆过滤器加速细粒度自监督学习的数据核心集采样，快近百倍且精度损失极小。
BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning
- 用布隆过滤器高效存储多层级特征，实现快速核心集检索。
- 采样时间减少98.5%，11个下游数据集平均精度仅下降0.83%。
- 适合大规模无标注细粒度数据的快速预处理，提升训练效率。
深度学习在特定领域细粒度识别任务中的成功高度依赖专家标注。开放集细粒度自监督学习（SSL）的目标是通过从大量无标注数据（开放集）中策略性地选取图像子集（核心集），来提升下游任务性能。本文提出一种新方法BloomCoreset，显著降低从开放集中采样核心集的时间，同时保持样本质量。该方法创新性地利用布隆过滤器作为哈希机制，以空间高效的方式存储由Open-CLIP捕获的细粒度数据的低层和高层特征，从而实现核心集的快速检索。为验证所采核心集的有效性，我们将该方法集成到当前最先进的细粒度自监督学习框架SimCore [1]中。所提算法在采样时间上相比基线方法大幅优化，减少98.5%，而在11个下游数据集上的平均精度仅下降0.83%。
原文摘要 · Abstract (English)
The success of deep learning in supervised fine-grained recognition for domain-specific tasks relies heavily on expert annotations. The Open-Set for fine-grained Self-Supervised Learning (SSL) problem aims to enhance performance on downstream tasks by strategically sampling a subset of images (the Core-Set) from a large pool of unlabeled data (the Open-Set). In this paper, we propose a novel method, BloomCoreset, that significantly reduces sampling time from Open-Set while preserving the quality of samples in the coreset. To achieve this, we utilize Bloom filters as an innovative hashing mechanism to store both low- and high-level features of the fine-grained dataset, as captured by Open-CLIP, in a space-efficient manner that enables rapid retrieval of the coreset from the Open-Set. To show the effectiveness of the sampled coreset, we integrate the proposed method into the state-of-the-art fine-grained SSL framework, SimCore [1]. The proposed algorithm drastically outperforms the sampling strategy of the baseline in SimCore [1] with a $98.5\%$ reduction in sampling time with a mere $0.83\%$ average trade-off in accuracy calculated across $11$ downstream datasets.
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