M2m: Imbalanced Classification via Major-to-minor Translation (2004.00431v2)

Abstract: In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples (e.g., images) from more-frequent classes. This simple approach enables a classifier to learn more generalizable features of minority classes, by transferring and leveraging the diversity of the majority information. Our experimental results on a variety of class-imbalanced datasets show that the proposed method improves the generalization on minority classes significantly compared to other existing re-sampling or re-weighting methods. The performance of our method even surpasses those of previous state-of-the-art methods for the imbalanced classification.

• The paper introduces M2m, a method that converts majority samples into synthetic minority examples to address class imbalance in deep learning.
• It employs a classifier-guided optimization and rejection mechanism to generate high-quality samples, refining decision boundaries.
• Experimental results on multiple imbalanced datasets show significant accuracy gains over re-sampling and state-of-the-art techniques like LDAM.

Imbalanced Classification via Major-to-minor Translation for Deep Neural Networks

The paper "M2m: Imbalanced Classification via Major-to-minor Translation" addresses the challenge of class imbalance in deep learning, a common problem in real-world datasets where certain classes dominate in frequency over others. This imbalance often leads to a bias in learning performance, adversely affecting the generalization capability of classifiers. The authors introduce a novel approach to enhance the representation of minority classes by translating samples from majority classes, thereby aiming to mitigate the data imbalance problem effectively.

Central to the proposed method, termed Major-to-minor Translation (M2m), is the intuitive yet straightforward concept of generating synthetic minority class samples. These samples are not merely augmented variants of existing minority data, as in traditional over-sampling techniques like SMOTE, but are rather translations from majority class samples. The translation is achieved using a trained classifier that serves as a guide, enabling a learning model to expand the decision boundaries for minority classes. This approach leverages the diverse feature space information available in majority classes to extrapolate and enrich the sparse feature representations of minority classes.

Methodology and Mechanism

The M2m method involves the creation of synthetic minority samples through an optimization process that translates a majority class sample into a minority class sample. This is performed using another classifier, trained on the imbalance dataset, to navigate the optimal transformation pathway. The concept of translating rather than generating from scratch helps ensure the newly synthesized samples capture the nuanced decision boundaries between classes that conventional augmentation might miss.

Three innovative components underpin the method:

1. Optimization Objective: It formulates a translation goal via optimizing towards the minority class confidence, while imposing a regularization term to dampen the initial class confidence in the majority class.
2. Sample Rejection Criterion: This rejects low-confidence samples based on a probabilistic threshold, ensuring high-quality inclusion of generated samples.
3. Optimal Seed Sampling: An approach determining which majority class samples should serve as seeds for the translation process, aiming to maximize generation outcomes while maintaining diversity.

The implementation of M2m in deep neural network training demonstrated superior performance improvement in balanced test accuracy over conventional techniques like re-weighting and re-sampling, even surpassing state-of-the-art methods such as LDAM.

Experimental Results

Extensive experimentation reveals that the M2m model significantly boosts generalization on multiple class-imbalanced datasets, including synthetically imbalanced CIFAR-10 and CIFAR-100, and naturally imbalanced datasets like CelebA and Twitter datasets. On the most challenging scenarios with extreme class imbalance, such as the Reuters dataset, M2m exhibits marked improvements, underscoring its efficacy in tackling variance caused by data scarcity in minority classes.

In quantitative figures, M2m consistently outperforms existing methods, offering notable precision gains in scenarios with high imbalance ratios, showing relative improvements such as 17.1% and 9.2% upon standard training and the LDAM approach, respectively.

Theoretical and Practical Implications

The theoretical underpinnings of M2m challenge the prevailing paradigm of data-specific augmentation, suggesting a role for adversarial-like feature inclusion in improving underrepresented class learning. Practically, M2m provides a framework that could seamlessly integrate into existing training pipelines, offering a corrective lens towards equity in dataset representation, which is crucial for achieving unbiased AI outcomes.

Future Developments

Future research could explore extending this method with more sophisticated generative mechanisms, such as GAN-based translations, to further capture the inter-class feature nuances. Moreover, deploying M2m in active learning and meta-learning frameworks could reveal deeper insights into its adaptability and potential in diverse AI applications.

Overall, this paper presents a substantive contribution to the field of imbalanced learning by balancing innovation with implementational simplicity, encouraging further exploration into cross-class learning paradigms.