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StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning

Tongjun Shi, Shuhao Zhang, Binbin Chen, Bingsheng He

TL;DR

This paper tackles the challenge of efficient, accurate stream learning under rapidly changing data distributions. It introduces StreamFP, a framework that uses dynamic, learnable fingerprints to jointly optimize coreset selection and rehearsal-buffer updates, while a fingerprint attunement module leverages pretrained Vision Transformer attention for lightweight adaptation. The three components—Fingerprint-based Coreset Selection, Fingerprint-based Buffer Update, and Fingerprint Attunement—work in concert to maintain model performance with high training throughput, validated across multiple real-world datasets and arrival rates. The approach yields substantial accuracy improvements and reduced forgetting, yielding practical benefits for real-time streaming applications and highlighting the potential to extend fingerprint-based strategies to other transformer architectures.

Abstract

Stream Learning (SL) requires models that can quickly adapt to continuously evolving data, posing significant challenges in both computational efficiency and learning accuracy. Effective data selection is critical in SL to ensure a balance between information retention and training efficiency. Traditional rule-based data selection methods struggle to accommodate the dynamic nature of streaming data, highlighting the necessity for innovative solutions that effectively address these challenges. Recent approaches to handling changing data distributions face challenges that limit their effectiveness in fast-paced environments. In response, we propose StreamFP, a novel approach that uniquely employs dynamic, learnable parameters called fingerprints to enhance data selection efficiency and adaptability in stream learning. StreamFP optimizes coreset selection through its unique fingerprint-guided mechanism for efficient training while ensuring robust buffer updates that adaptively respond to data dynamics, setting it apart from existing methods in stream learning. Experimental results demonstrate that StreamFP outperforms state-of-the-art methods by achieving accuracy improvements of 15.99%, 29.65%, and 51.24% compared to baseline models across varying data arrival rates, alongside a training throughput increase of 4.6x.

StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning

TL;DR

This paper tackles the challenge of efficient, accurate stream learning under rapidly changing data distributions. It introduces StreamFP, a framework that uses dynamic, learnable fingerprints to jointly optimize coreset selection and rehearsal-buffer updates, while a fingerprint attunement module leverages pretrained Vision Transformer attention for lightweight adaptation. The three components—Fingerprint-based Coreset Selection, Fingerprint-based Buffer Update, and Fingerprint Attunement—work in concert to maintain model performance with high training throughput, validated across multiple real-world datasets and arrival rates. The approach yields substantial accuracy improvements and reduced forgetting, yielding practical benefits for real-time streaming applications and highlighting the potential to extend fingerprint-based strategies to other transformer architectures.

Abstract

Stream Learning (SL) requires models that can quickly adapt to continuously evolving data, posing significant challenges in both computational efficiency and learning accuracy. Effective data selection is critical in SL to ensure a balance between information retention and training efficiency. Traditional rule-based data selection methods struggle to accommodate the dynamic nature of streaming data, highlighting the necessity for innovative solutions that effectively address these challenges. Recent approaches to handling changing data distributions face challenges that limit their effectiveness in fast-paced environments. In response, we propose StreamFP, a novel approach that uniquely employs dynamic, learnable parameters called fingerprints to enhance data selection efficiency and adaptability in stream learning. StreamFP optimizes coreset selection through its unique fingerprint-guided mechanism for efficient training while ensuring robust buffer updates that adaptively respond to data dynamics, setting it apart from existing methods in stream learning. Experimental results demonstrate that StreamFP outperforms state-of-the-art methods by achieving accuracy improvements of 15.99%, 29.65%, and 51.24% compared to baseline models across varying data arrival rates, alongside a training throughput increase of 4.6x.
Paper Structure (23 sections, 4 theorems, 15 equations, 4 figures, 6 tables, 2 algorithms)

This paper contains 23 sections, 4 theorems, 15 equations, 4 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Preliminaries
  4. Problem Definition
  5. System Design
  6. Stage 1: Coreset Selection
  7. Stage 2: Model Update
  8. Stage 3: Buffer Update
  9. Fingerprint-based Coreset Selection
  10. Fingerprint-based Buffer Update
  11. Fingerprint Attunement
  12. Experiments
  13. Methodologies
  14. Observation Summary
  15. Experiment Results
  16. ...and 8 more sections

Key Result

Theorem 1

With probability at least $1-\delta$, the coreset $C^t$ satisfies: where $\text{cost}(X) = \frac{1}{|X|}\sum_{x\in X} d(x)$, representing the average angular distance, $d(x) = \arccos(\text{sim}(x,P))$, and $\varepsilon = O(\sqrt{\log(1/\delta)/(\sigma b)})$.

Figures (4)

  • Figure 1: The overview of StreamFP. Three components—Fingerprint-based Data Selection, Fingerprint-based Buffer Update, and Fingerprint Attunement—work synergistically to enhance training efficiency and preserve accuracy in stream learning.
  • Figure 2: Comparison of existing work vs. our approach: Current methods bias data selection towards dominant clips in diverse streaming batches, reducing model generalizability. Our approach uses knowledge-inheriting fingerprints to create a diverse, representative coreset and buffer, enhancing model performance.
  • Figure 3: Sensitivity study on Stream-51 with $\lambda$=6028.
  • Figure 4: Heatmap of gradient correlations for diverse tasks on Stream-51.

Theorems & Definitions (9)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Theorem 1: Coreset Quality Guarantee
  • Theorem 2
  • Theorem 3: Coreset Quality Guarantee
  • Theorem 4