Summary of Difficulty-aware Balancing Margin Loss For Long-tailed Recognition, by Minseok Son et al.
Difficulty-aware Balancing Margin Loss for Long-tailed Recognition
by Minseok Son, Inyong Koo, Jinyoung Park, Changick Kim
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents a novel approach to deep neural network training when dealing with severely imbalanced data, where classes have vastly different sample sizes. The authors introduce the difficulty-aware balancing margin (DBM) loss function, which addresses both class imbalance and instance difficulty variation within each class. DBM loss is composed of two components: a class-wise margin for learning bias mitigation and an instance-wise margin assigned to hard positive samples based on their individual difficulty. This approach improves class discriminativity by assigning larger margins to more challenging instances. The authors demonstrate the effectiveness of their method by combining it with existing approaches, showcasing consistent performance improvements across various long-tailed recognition benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary When deep neural networks are trained with severely imbalanced data, they often struggle to recognize classes with few samples. Previous studies tried to balance this learning using known sample distributions, but these methods overlook the difficulty variation within each class. This paper proposes a new approach called DBM loss, which considers both class imbalance and instance difficulty. The authors divide the DBM loss into two parts: one that helps with learning bias caused by imbalanced classes, and another that assigns more attention to harder samples within each class. This makes it easier for networks to recognize hard instances and improves performance across different datasets. |
Keywords
» Artificial intelligence » Attention » Loss function » Neural network
