The field of machine learning, particularly in the context of imbalanced data classification and long-tailed recognition, is witnessing significant advancements aimed at improving model performance and fairness. A common theme across recent research is the development of novel loss functions and data augmentation techniques that address the challenges posed by class imbalance and instance difficulty. These approaches aim to enhance class discriminativity and model generalization by considering both class-level and instance-level characteristics. Additionally, there is a growing interest in leveraging graph-based methods and algebraic evaluation techniques to improve the quality of synthetic data generation and group decision-making processes, respectively. These innovations not only contribute to the development of more reliable classification systems but also offer insights into the underlying mechanisms of model bias and correction.

Noteworthy Papers

• Difficulty-aware Balancing Margin Loss for Long-tailed Recognition: Introduces a novel loss function that addresses both class imbalance and instance difficulty, improving performance across benchmarks.
• Synthetic Tabular Data Generation for Imbalanced Classification: Proposes a novel technique for enhancing the quality of synthetic data generation by introducing an overlap class, significantly improving synthesizer and classifier performance.
• GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification: Presents a graph-based oversampling method that leverages attention mechanisms to generate informative synthetic minority samples, outperforming state-of-the-art techniques.
• Prior2Posterior: Model Prior Correction for Long-Tailed Learning: Offers a post-hoc approach to correct imbalanced prior in trained models, achieving new state-of-the-art performance on benchmark datasets.