Cost-Effective Active Learning for Deep Image Classification (1701.03551v1)

Abstract: Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human efforts. In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner. Our approach advances the existing active learning methods in two aspects. First, we incorporate deep convolutional neural networks into active learning. Through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. Second, we present a cost-effective sample selection strategy to improve the classification performance with less manual annotations. Unlike traditional methods focusing on only the uncertain samples of low prediction confidence, we especially discover the large amount of high confidence samples from the unlabeled set for feature learning. Specifically, these high confidence samples are automatically selected and iteratively assigned pseudo-labels. We thus call our framework "Cost-Effective Active Learning" (CEAL) standing for the two advantages.Extensive experiments demonstrate that the proposed CEAL framework can achieve promising results on two challenging image classification datasets, i.e., face recognition on CACD database [1] and object categorization on Caltech-256 [2].

• The paper introduces CEAL, which integrates CNNs with active learning by selecting low-confidence samples for manual annotation while pseudo-labeling high-confidence ones.
• It demonstrates that using CEAL reduces labeling requirements significantly, achieving 91.5% accuracy on CACD with only 63% of the training samples labeled compared to traditional methods.
• The framework bridges active learning and deep learning to deliver a scalable, cost-effective solution for enhancing image classification in resource-intensive real-world applications.

Cost-Effective Active Learning for Deep Image Classification

The paper "Cost-Effective Active Learning for Deep Image Classification" by Keze Wang et al. addresses the challenge of reducing the need for large labeled datasets in training deep convolutional neural networks (CNNs) for image classification tasks. The authors propose a novel framework termed Cost-Effective Active Learning (CEAL) which integrates active learning (AL) with deep learning methodologies to optimize classifier accuracy with minimal manual annotation.

Key Contributions and Methodology

The CEAL framework innovatively combines CNNs with an active learning paradigm, advancing the traditional AL approaches through two main contributions:

1. Integrated Deep Learning and Active Learning: The framework simultaneously updates a classifier with progressively annotated informative samples, leveraging the representational power of deep CNNs. This integration allows for the optimization of both feature representation and classifier accuracy in tandem, addressing the limitations of previous AL methods that relied on hand-crafted features.
2. Cost-Effective Sample Selection: Unlike traditional uncertainty-based AL methods, CEAL introduces a scheme to leverage both low and high confidence samples from the unlabeled dataset. While minority samples with low prediction confidence are selected for manual annotation, majority high confidence samples are automatically pseudo-labeled. This dual strategy ensures robust feature learning for the CNN without excessive human labeling effort, exploiting the large volume of unlabeled data effectively.

Experimental Evaluation

The framework's efficacy is demonstrated on two challenging datasets: CACD for face recognition and Caltech-256 for object categorization. The results reflect the superiority of CEAL over baseline methods, particularly in terms of reducing the need for labeled data while maintaining high classification accuracy. For instance, to achieve 91.5% accuracy on the CACD dataset, CEAL only requires labeling 63% of the training samples, compared to 81% and 99% required by AL_RANDOM and TCAL methods, respectively.

Theoretical and Practical Implications

This research presents significant implications both theoretically and practically. Theoretically, it progresses the field of active learning by addressing the inconsistency issues between the process pipelines of AL methods and CNNs, offering a unified framework that optimizes both components. Practically, CEAL provides a promising avenue for real-world applications where data collection and labeling are resource-intensive, such as in medical imaging and large-scale visual recognition tasks.

Future Directions

The proposed framework opens several avenues for future research. Incorporating CEAL into larger-scale datasets like ImageNet could further validate its scalability and efficiency. Additionally, expanding this framework to tackle multi-label classification problems or applying it to video data could broaden its applicability.

In conclusion, the paper presents a compelling approach to deep image classification, effectively combining active learning with deep neural networks to minimize manual annotation efforts while enhancing classifier performance.