Deep Imbalanced Learning for Face Recognition and Attribute Prediction (1806.00194v2)

Abstract: Data for face analysis often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary deep learning methods typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain inter-cluster margins both within and between classes. This tight constraint effectively reduces the class imbalance inherent in the local data neighborhood, thus carving much more balanced class boundaries locally. We show that it is easy to deploy angular margins between the cluster distributions on a hypersphere manifold. Such learned Cluster-based Large Margin Local Embedding (CLMLE), when combined with a simple k-nearest cluster algorithm, shows significant improvements in accuracy over existing methods on both face recognition and face attribute prediction tasks that exhibit imbalanced class distribution.

• The paper introduces CLMLE, a novel method that enforces angular margins between local clusters to address imbalanced data challenges.
• It leverages cluster-based embeddings to enhance decision boundaries, improving performance in both face recognition and attribute prediction.
• Empirical results on benchmarks like LFW, YTF, and CelebA demonstrate state-of-the-art accuracy and efficiency with reduced training data.

Overview of "Deep Imbalanced Learning for Face Recognition and Attribute Prediction"

The paper "Deep Imbalanced Learning for Face Recognition and Attribute Prediction" presents an innovative approach addressing the challenges of imbalanced data distribution in deep learning, specifically in the domains of face recognition and attribute prediction. The authors introduce a novel methodology called Cluster-based Large Margin Local Embedding (CLMLE) which enhances the discriminative capability of learned features by focusing on balancing the decision boundaries in locally imbalanced data neighborhoods.

Key Contributions

1. Introduction of CLMLE: The paper proposes CLMLE, a method that deploys angular margins between cluster representations on a hypersphere manifold. This approach is designed to mend the representation learning shortcomings in handling class imbalance by ensuring local class boundaries are balanced.
2. Cluster Utilization: CLMLE effectively exploits the multimodal nature of class distributions by leveraging cluster assignments for each class. This significantly mitigates the inherent imbalance in local neighborhood data structures, ensuring robust learning even in diverse class distributions.
3. Enhanced Computational Efficiency: Unlike prior methods like triplet or quintuplet loss which suffer from sampling inefficiencies, CLMLE focuses on the entire cluster distribution, thus enhancing learning coherency and convergence speed. The complexity of training is maintained at a linear scale relative to the dataset, making the approach efficient for large-scale deployment.
4. Robust Performance on Benchmarks: The approach has been empirically validated on large-scale benchmarks like LFW, YTF, and MegaFace Challenge, achieving state-of-the-art results with significantly reduced training data when compared to existing methodologies. The effectiveness of CLMLE is further showcased through superior balanced accuracy on tasks like face attribute prediction on the CelebA dataset.

Implications and Future Directions

Practical Implications: The CLMLE model offers a significant advancement in the application of face recognition and attribute prediction tasks, where class imbalance is a prevalent challenge. By enabling more balanced decision boundaries locally, the model promises improved accuracy and robustness in real-world applications where data collection often results in skewed distributions.

Theoretical Advancement: The paper provides critical insights into multimodal class representation learning. By ensuring large margins between local clusters, the framework transcends beyond traditional unimodal assumptions, offering a paradigm shift that could inspire further research into imbalanced learning scenarios across different domains.

Potential Future Work: Future studies might explore the adaptation of CLMLE to other domains plagued by class imbalance, such as natural language processing and medical image analysis. Moreover, integrating CLMLE with ensembles or hybrid models could potentially enhance performance further. There is also an open avenue for investigating the impact of alternative similarity metrics or margin-derived constraints, potentially extending the applicability of this method.

Overall, this work by Huang et al. represents a substantive contribution to the field of computer vision by effectively tackling the imbalanced data problem, thus paving the way for more generalized, robust applications of deep learning in real-world settings.