FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discovery
Quick Answer
FunnelAL is a novel active learning system that enhances single-class discovery by using a multi-stage funnel architecture, achieving superior F1 scores and annotation efficiency across three benchmarks.
Quick Take
It outperforms traditional methods like GAL and PF-MA, especially under realistic annotator error rates, by effectively narrowing down candidate samples and refining selection through iterative feedback.
Key Points
- FunnelAL achieves best final F1 scores on three diverse image classification benchmarks.
- It demonstrates the highest annotation efficiency, ranking first in AULC.
- The system uses a three-stage process: retrieval scoring, ranking, and feedback refinement.
- FunnelAL outperforms GAL and PF-MA while reducing annotation rounds.
- Under realistic annotator errors, it maintains top performance compared to classical methods.
Paper Resources
Source Excerpt
We present FunnelAL, a retrieve-then-rank active learning system for single-class discovery, which adapts the multi-stage funnel architecture of industrial recommender systems to data annotation. Large-scale supervised learning faces two challenges: efficiently finding relevant samples in a massive corpus, and distinguishing true positives from visually confusable negatives when embeddings do not cleanly separate classes. Conventional active learning offers a principled framework for reducing an
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