Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective (2002.10826v3)

Abstract: This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have significantly small spatial span, due to the lack of intra-class diversity. This uneven distribution between head and tail classes distorts the overall feature space, which compromises the discriminative ability of the learned features. Intuitively, we seek to expand the distribution of the tail classes by transferring from the head classes, so as to alleviate the distortion of the feature space. To this end, we propose to construct each feature into a "feature cloud". If a sample belongs to a tail class, the corresponding feature cloud will have relatively large distribution range, in compensation to its lack of diversity. It allows each tail sample to push the samples from other classes far away, recovering the intra-class diversity of tail classes. Extensive experimental evaluations on person re-identification and face recognition tasks confirm the effectiveness of our method.

• The paper introduces a 'feature cloud' concept to expand tail class diversity and improve intra-class discrimination.
• It proposes a learnable embedding augmentation that transfers angular variance from head to tail classes to boost feature effectiveness.
• Experimental results on person re-identification and face recognition demonstrate significant gains in mAP and Rank-1 accuracy.

Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective

The paper "Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective" addresses the challenge of learning discriminative features from long-tailed datasets, where the number of samples per class varies significantly. This poses a problem in the feature space, as head classes with abundant samples have a broad distribution, while tail classes with limited samples exhibit a narrow distribution. The resultant unbalance leads to compromised feature discrimination for machine learning models.

Key Contributions and Methodology

1. Feature Cloud Concept: The authors introduce the notion of a "feature cloud" to enhance the intra-class diversity of tail classes. By expanding the distribution range of tail classes in the feature space, each instance from tail classes can maintain a larger influence, allowing it to push samples from other classes away, which inherently aims to compensate for their lack of diversity.
2. Learnable Embedding Augmentation: The research proposes a mechanism for transferring the intra-class variance from head classes to tail classes. Specifically, they model the distribution of the angles between samples and their class centers to statistically analyze and bridge the gap in angular variance between head and tail classes, thus aiming to improve the discriminative ability of the learned features.
3. A Flexible Framework: By avoiding an explicit distinction between head and tail classes, the framework adapts naturally to the distribution of the dataset, providing an implicit yet effective method to mitigate the imbalance, thereby enhancing the generalization of the trained model.
4. Experimental Validation: Extensive experimentation on person re-identification and face recognition tasks demonstrates the approach's efficacy. Notably, improvements in tasks performed on long-tailed data through significant gains in mAP and Rank-1 accuracy metrics were observed. For instance, in the setting of person re-identification: $\left \langle H20,S4 \right \rangle$, the intra-class angular variance was noteworthy and demonstrated improvements over baseline methods like CosFace and ArcFace.

Implications and Future Directions

The research offers a promising avenue for handling long-tailed distributions in deep representation learning tasks by innovatively addressing intra-class diversity issues. From a practical perspective, the proposed techniques can be integrated into the prevailing feature learning models to enhance their performance on skewed datasets.

On a theoretical level, the model's adaptability and intra-class variance transfer methods could pave the way for exploring other class imbalance challenges. Future research could focus on refining the framework to be more robust under extreme class imbalance scenarios, as well as assessing its applicability across diverse domains such as natural language processing, where data imbalance is prevalent.

Additionally, the full version's capability to automatically adjust without manual intervention for defining head and tail classes underscores the potential for broader applicability of these techniques, streamlining their integration into various machine learning pipelines. Exploring the method's scalability across even larger datasets, or combining it with adversarial approaches for data augmentation, could yield significant advancements in feature learning under imbalanced conditions.

In conclusion, this paper provides a well-articulated methodology that enhances learning from long-tailed data distributions, paving the way for further research into adaptive feature learning paradigms, with substantial implications for a wide range of AI applications.