- The paper presents a dynamic framework that integrates sampling and loss schedulers to progressively balance data distribution and focus on challenging examples.
- It effectively combines curriculum learning with metric learning to develop robust feature embeddings and improve classification accuracy.
- The framework outperforms existing methods on benchmarks like CelebA and RAP, demonstrating adaptability and computational efficiency in addressing class imbalance.
Dynamic Curriculum Learning for Imbalanced Data Classification
The paper introduces an innovative methodological framework referred to as Dynamic Curriculum Learning (DCL) designed to address the pervasive challenge of class imbalance in human attribute analysis within computer vision. This framework notably incorporates dynamic adjustments in training strategies, leveraging the concept of curriculum learning, which orchestrates learning from simpler to more complex tasks in a systematic manner.
At the core of DCL are two curriculum schedulers: the sampling scheduler and the loss scheduler. The sampling scheduler dynamically transitions the data distribution in each batch from an initially imbalanced state towards a balanced state. Concurrently, it shifts the emphasis from easy examples to harder ones progressively. This is crucial for mitigating the overfitting often encountered with traditional oversampling techniques that might disproportionately favor minority classes while neglecting valuable information from majority classes. The loss scheduler dynamically scopes the task importance between classification loss, formulated as cross-entropy, and metric learning loss, ensuring that the feature representations learned are conducive for both class discrimination and balanced metric learning. The loss scheduler initially prioritizes metric learning to develop robust feature embeddings and subsequently emphasizes classification accuracy as training progresses.
DCL demonstrates state-of-the-art performance on benchmarks such as the CelebA and RAP datasets, both of which present significant class imbalances across their numerous attributes. The results underscore the framework's capacity to significantly enhance class-balanced accuracy, particularly evident in attributes with high imbalance ratios. On CelebA, the method outperformed existing solutions such as Selective Learning (SL) and Class Rectification Loss I (CRL-I) by operating more effectively across a range of imbalance scenarios. Similarly, on RAP, the introduction of DCL facilitates a marked improvement over prior efforts like the LG-Net and HP-net architectures, especially for attributes with extreme imbalance levels.
Beyond its empirical success, DCL's integration of two-level curriculum schedulers and metric learning elaborates a flexible architecture capable of generalizing across a variety of imbalance contexts, thus promising extensibility to other domains beyond facial and pedestrian attribute analysis. Importantly, DCL also enhances training efficiency, avoiding the computational burden typical of complex sampling techniques and clustering approaches like LMLE and CLMLE, making it a computationally appealing alternative.
For future developments, the inherent adaptability of DCL opens avenues for exploration in semi-supervised and unsupervised domains where label scarcity exacerbates imbalance issues. The application of this framework to broader, perhaps non-visual domains, while maintaining its computational feasibility, represents an intriguing area of further research. By advancing methods that are adaptive and data-driven, DCL invites a deeper inquiry into its integration with other learning paradigms, including reinforcement and active learning, potentially advocating for richer, context-aware AI systems that can autonomously prioritize and balance learning tasks.