Balancing Knowledge Distillation for Imbalance Learning with Bilevel Optimization

Researchers introduce BiKD, a bilevel optimization framework that dynamically adjusts the balance between hard and soft losses in knowledge distillation for imbalanced datasets. The method uses a weight generation network guided by a balanced validation set to assign per-sample adaptive weights, significantly improving performance on long-tailed datasets like CIFAR-10/100 compared to existing approaches.

This research addresses a fundamental challenge in machine learning: effectively training neural networks on imbalanced datasets while leveraging knowledge distillation techniques. Knowledge distillation has become a cornerstone technique for deploying efficient models in resource-constrained environments, but its effectiveness deteriorates when training data exhibits severe class imbalance—a common real-world scenario. The paper's contribution lies in recognizing that static weighting schemes between hard targets (ground truth labels) and soft targets (teacher model outputs) fail to accommodate the varying learning needs across different samples during training.

The BiKD framework represents an evolution in adaptive learning approaches by introducing sample-level granularity rather than class-level reweighting. By incorporating a separate weight generation network that operates on a balanced validation set, the method achieves two critical advantages: it maintains computational efficiency while capturing nuanced per-sample dynamics, and it prevents the student model from becoming overly constrained by either loss component. The multi-step SGD optimization strategy further enhances computational efficiency, addressing practical deployment concerns.

For practitioners developing machine learning systems, this work carries substantial implications. Imbalanced datasets pervade real-world applications, from fraud detection to medical imaging, making robust distillation techniques economically valuable. Organizations deploying edge AI models can leverage this approach to maintain model performance while reducing computational overhead. The experimental validation on standard benchmarks provides empirical confidence in the method's efficacy.

Future research directions include extending BiKD to larger-scale datasets, examining its behavior with extreme imbalance ratios, and investigating how the framework generalizes across different neural network architectures and domain-specific applications.

• →BiKD dynamically balances hard and soft losses at the sample level using adaptive per-sample weights guided by a balanced validation set
• →The method outperforms existing balanced distillation approaches on long-tailed CIFAR-10/100 benchmarks across multiple imbalance factors
• →Sample-wise weight generation enables more granular adaptation than traditional class-level reweighting strategies in imbalanced learning
• →Multi-step SGD optimization improves both accuracy and computational efficiency of the weight generation network
• →The framework allows student models to relax constraints from both loss components, improving overall training stability