Source-linked AI summary
Diverse mini-batch Active Learning
Fedor Zhdanov
TL;DR
The paper addresses the cost of obtaining labeled data and the impracticality of sequential Active Learning for modern deep models. It proposes a mini-batch method combining informativeness and diversity through K-means clustering, achieving comparable or better performance with greater scalability than prior approaches.
Problem
Modern Deep Learning requires large labeled datasets, while sequential Active Learning selection and retraining are impractical for mini-batch annotation.
Method
The method selects mini-batches using both informativeness and diversity, linking the selection objective to Facility Location and solving it with K-means clustering.
Results
Diversity-enhancing approaches slightly or significantly outperform uncertainty sampling, while the proposed approach is comparable to previously published techniques and more scalable.
Takeaways & Limitations
The approach provides a scalable way to select diverse informative mini-batches across datasets and models of varying complexity.
Takeaways & Limitations
Further work is needed to reduce dependence on the pre-filtering parameter and extend the approach to scalable non-Euclidean distances.
Abstract
from arXiv · showhide
We study the problem of reducing the amount of labeled training data required to train supervised classification models. We approach it by leveraging Active Learning, through sequential selection of examples which benefit the model most. Selecting examples one by one is not practical for the amount of training examples required by the modern Deep Learning models. We consider the mini-batch Active Learning setting, where several examples are selected at once. We present an approach which takes into account both informativeness of the examples for the model, as well as the diversity of the examples in a mini-batch. By using the well studied K-means clustering algorithm, this approach scales better than the previously proposed approaches, and achieves comparable or better performance.
1 Introduction
The paper motivates mini-batch Active Learning because sequential selection and retraining are impractical for modern deep models. It proposes selecting batches that combine informativeness with diversity, using a scalable K-means-based approach.
- Motivation: Large quantities of labeled data are costly to collect for modern Deep Learning algorithms.The paper highlights annotation cost as a motivation for reducing labeled-data requirements.
- Motivation: Active Learning reduces labeling needs by suggesting examples that provide the model with the most information.Its goal is to reach the accuracy of models trained on the full dataset with fewer labeled examples.
- Mini-batch setting: Sequential example selection is impractical because retraining modern Deep Learning models can take hours or days.Many models must be retrained on the whole labeled dataset after each new label.
- Mini-batch setting: Selecting the top B informative examples at once can produce overly similar batches, especially in redundant datasets.The paper therefore argues that more diverse batches may benefit the model more.
- Proposed approach: The proposed algorithm jointly uses example informativeness and pairwise diversity to select the next annotation mini-batch.The distance metric and informativeness measure can be selected according to user preferences.
- Proposed approach: K-means provides a more scalable solution to diverse selection than previously studied approaches.The paper connects diverse selection to Facility Location and evaluates the procedure across several model families.
- Empirical contribution: Margin-based uncertainty outperformed random selection in all reported cases.The paper contrasts this result with entropy-based uncertainty sampling used in many other studies.
2 Related Work
Related approaches address informativeness, clustering, sequential batch construction, or diversity optimization, but each has limitations involving diversity, tractability, retraining cost, or scalability.
- Existing approaches: Informative-and-representative selection methods often use specialized objectives and learning algorithms without modeling mini-batch diversity.The paper positions its method as compatible with these approaches through their informativeness scores.
- Existing approaches: Semi-supervised clustering can spend effort building dataset-wide clusters that are already clear to the learning model.This approach constructs a separate classifier from the model being trained.
- Existing approaches: Sequential batch construction based on hypothesized labels is computationally difficult because all label assignments are not tractable to consider.Using probable labels can diverge substantially from the actual labeling.
- Existing approaches: Greedy retraining after every selected example may be unsuitable for learning methods with costly retraining.The related approach also narrows possible label assignments and may diverge from the actual labeling.
- Diversity methods: Submodular diversity methods optimize distance-based objectives, whereas this paper uses clustering and explicitly combines diversity with informativeness.The paper presents clustering as a route to better scalability than submodular optimization.
3 Problem Setup
The selection problem chooses a diverse subset of unlabeled examples by minimizing a Facility Location objective. The paper reformulates this problem for K-means, yielding a scalable procedure that can incorporate informativeness scores.
- Selection problem: At each Active Learning step, the method selects B examples from the N unlabeled examples for annotation and further training.The selected batch is represented as S, with |S| = B ≤ N.
- Selection problem: The diversity objective uses distances between unlabeled examples to construct a representative batch.The distance metric d(x_i, x_j) can be chosen according to the application.
- Facility Location: This objective is a Facility Location formulation whose exact optimization is NP-hard.Approximate algorithms are therefore required.
- K-means solution: K-means solves the reformulated selection problem using Euclidean distance and has complexity O(NBI).Here N is the unlabeled-set size, B the batch size, and I the number of iterations.
- K-means solution: K-means is presented as more scalable than submodular optimization, which requires O(N^2) computations.Precomputing the distance matrix also requires higher complexity and memory for practical speed.
- K-means solution: K-means assigns points to cluster centers and selects examples closest to those centers.This produces labeled examples representing the selected clusters.
- Informativeness: The objective can incorporate informativeness scores s_i ∈ [0,1] supplied by an Active Learning method.The framework can use measures such as uncertainty sampling or Mutual Information-based selection.
- Informativeness: During weighted K-means optimization, points remain assigned to their closest cluster centers while cluster centers are updated.The method uses this iterative procedure to optimize the weighted objective.
4 Experimental Results
The experiments evaluate DBAL across text and image classification tasks using margin-based uncertainty, clustering, and diversity-aware baselines. Diversity-based selection generally matches or outperforms uncertainty sampling while K-means clustering provides substantial scalability advantages.
- Experimental setup: DBAL evaluates margin-based uncertainty with random, uncertainty, clustering, weighted clustering, submodular, and FASS selection methods across text and image classification datasets.The experiments use generalized linear models, multilayer perceptrons, and Deep CNNs, with batch sizes of 100 except 1000 for CIFAR-10.
- Experimental setup: K-means clustering has linear complexity O(NBI), improving substantially over the O(N^2) computations required for submodular optimization.The clustering procedure operates on prefiltered informative examples rather than the entire unlabeled set.
- Browse Nodes UK: β = 10 diversity-based methods significantly outperform uncertainty sampling on the Browse Nodes UK dataset, while Clustered(10) finishes in 10 seconds versus about 4700 seconds for FASS(10).With β = 50, diversity-based methods eventually underperform uncertainty sampling after about 300 labeled examples, while weighted clustering remains better until about 500 examples.
- 20 Newsgroups: On 20 Newsgroups, diversity-aware methods perform slightly better than uncertainty sampling, while K-means with β = 50 is comparable to submodular methods and significantly faster.For β = 10, K-means performs on the lower range of the submodular methods’ confidence interval.
- MNIST: On MNIST, all diversity-based methods significantly outperform uncertainty sampling, and the proposed method performs as well as Submodular and better than FASS at the same β.Using β = 50 outperforms diversity-based methods with β = 10.
- MNIST: Selecting the first MNIST batch with K-means rather than randomly yields higher accuracy during the first two steps than all methods using random first-batch selection.The standard comparison selects the first batch randomly, whereas Figure 4 examines clustering from the beginning.
- CIFAR-10: On CIFAR-10, diversity-based selection slightly outperforms uncertainty sampling, and weighted clustering outperforms non-weighted clustering.CIFAR-10 uses a ResNet Deep Convolutional Neural Network and a batch size of 1000.
5 Discussion
The paper presents a scalable diversity-enhancing approach for mini-batch Active Learning, linked to Facility Location, and evaluates it across datasets and model complexities. Across experiments, diversity-based methods outperform uncertainty sampling while remaining comparable to prior techniques; future work targets pre-filtering dependence and non-Euclidean distances.
- 5 Discussion: The proposed approach increases diversity in mini-batch Active Learning and links the selection problem to Facility Location.It is evaluated with models ranging from generalized linear models to Deep CNNs.
- 5 Discussion: Diversity-enhancing approaches slightly or significantly outperform uncertainty sampling across experiments and achieve comparable performance to previously published techniques while being more scalable.The paper also reports the efficiency of its selected uncertainty-sampling baseline.
- 5 Discussion: Future research should reduce dependence on the pre-filtering parameter and test scalable K-means analogues for non-Euclidean distances.