Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets (2202.02794v4)

Abstract: Investigating active learning, we focus on the relation between the number of labeled examples (budget size), and suitable querying strategies. Our theoretical analysis shows a behavior reminiscent of phase transition: typical examples are best queried when the budget is low, while unrepresentative examples are best queried when the budget is large. Combined evidence shows that a similar phenomenon occurs in common classification models. Accordingly, we propose TypiClust -- a deep active learning strategy suited for low budgets. In a comparative empirical investigation of supervised learning, using a variety of architectures and image datasets, TypiClust outperforms all other active learning strategies in the low-budget regime. Using TypiClust in the semi-supervised framework, performance gets an even more significant boost. In particular, state-of-the-art semi-supervised methods trained on CIFAR-10 with 10 labeled examples selected by TypiClust, reach 93.2% accuracy -- an improvement of 39.4% over random selection. Code is available at https://github.com/avihu111/TypiClust.

Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

The paper, "Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets," explores the relationship between budget size and strategy selection in active learning (AL). The authors demonstrate through theoretical analysis and empirical results that typical and unrepresentative examples offer varying benefits depending on the budget size for labeled examples. The discussion builds upon concepts from data annotation and machine learning theory and contributes to improving learning efficiency, particularly in the context of low-budget learning environments.

Core Findings

1. Theoretical Analysis: The paper proposes a mixture model simulating independent learning from different data regions. Results demonstrate a phase-transition-like behavior where over-sampling typical data points benefits the low-budget regime, but when more examples are available, the focus should lean toward atypical examples. This aligns with observed behaviors in linear classifiers and demonstrates applicability to neural networks.
2. TypiClust Strategy: The authors introduce TypiClust, a novel AL strategy leveraging self-supervised representation learning and clustering to promote typicality and diversity. TypiClust performs clustering on the feature space and selects samples with the highest density from each cluster, aiming to represent data better without requiring large initial labeled sets.
3. Empirical Results: An extensive evaluation using varied image datasets, including CIFAR-10, CIFAR-100, TinyImageNet, and ImageNet subsets, attests to TypiClust's effectiveness. In the fully-supervised and semi-supervised frameworks, TypiClust outperforms traditional uncertainty-based methods in the low-budget regime, showing significant accuracy improvements. In some cases, gains exceeded 39% over baseline strategies in semi-supervised contexts, affirming TypiClust's efficacy in leveraging abundant unlabeled data.
4. Initial Pool Selection: The paper stresses the importance of starting with a representative initial pool to maximize learning outcomes when no pre-labeled data exists. Even when TypiClust is subjected to random initial selection, it effectively adapts and improves performance over competing methods, indicating robustness and flexibility in real-world applications.

Implications and Future Directions

The findings have practical implications for domains where collecting large annotated datasets is financially or logistically prohibitive. TypiClust's reliance on typical examples initially and adaptability across budget conditions makes it especially suited for specialized fields such as medical imaging or low-resource languages. On a theoretical level, the phase-transition-like model challenges conventional AL methods focused solely on uncertainty sampling, advocating a strategic shift based on the available budget.

Future research might explore the precise delineation of 'low' and 'high' budgets within specific applications, refining strategies to better accommodate domain-specific requirements. Additionally, integrating TypiClust within broader semi-supervised learning frameworks using novel representation learning approaches could further enhance its applicability and efficiency.

Overall, the paper contributes substantively to the field of active learning by guiding modelers toward more effective, data-efficient training regimens aligned with resource constraints. The convergence of empirical evidence and theoretical insights provides a foundation on which future AL methodologies can be innovatively designed and adapted.