$V_kD:$ Improving Knowledge Distillation using Orthogonal Projections
<|reference_start|>$V_kD:$ Improving Knowledge Distillation using Orthogonal Projections: Knowledge distillation is an effective method for training small and efficient deep learning models. However, the efficacy of a single method can degenerate when transferring to other tasks, modalities, or even other architectures. To address this limitation, we propose a novel constrained feature distillation method. This method is derived from a small set of core principles, which results in two emerging components: an orthogonal projection and a task-specific normalisation. Equipped with both of these components, our transformer models can outperform all previous methods on ImageNet and reach up to a 4.4% relative improvement over the previous state-of-the-art methods. To further demonstrate the generality of our method, we apply it to object detection and image generation, whereby we obtain consistent and substantial performance improvements over state-of-the-art. Code and models are publicly available: https://github.com/roymiles/vkd<|reference_end|>
@article{miles2024$v_kd:$,
    title={$V_kD:$ Improving Knowledge Distillation using Orthogonal Projections},
    author={Roy Miles, Ismail Elezi, Jiankang Deng},
    journal={arXiv preprint arXiv:2403.06213},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06213},
    primaryClass={cs.CV cs.AI}
}