VkD: 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.