Streamlined Knowledge Distillation

Logit-based Knowledge Distillation (KD) has emerged as a lightweight alternative to feature-based KD. Recent logitbased methods often rely on multi-knowledge alignment and relational modeling. These methods are often inefficient due to redundant objectives, suboptimal transformations, and poorly designed loss functions. Motivated by these issues, we propose Streamlined Knowledge Distillation (SKD), a simple yet effective logit-based method that transfers only two essential forms of knowledge without requiring additional alignment or relational modeling. Specifically, SKD transfers instance-wise knowledge via Kullback-Leibler divergence and direction-wise knowledge by aligning the Gramian matrix of normalized logits. For the latter, we introduce a Mahalanobis distance-based direction-wise loss stabilized through Tikhonov regularization and Cholesky decomposition. This direction-wise loss accounts for variance and correlation in the output space and, as we formally show, is equivalent to the L2-norm in a covariancewhitened space. Extensive experiments demonstrate that SKD consistently outperforms existing logit-based methods and even surpasses feature-based methods, despite its simpler design. Code is available at https://github. com/HyunJunSik/StreamLined.