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VkD: Improving Knowledge Distillation using Orthogonal Projections

Roy Miles · Ismail Elezi · Jiankang Deng

Arch 4A-E Poster #107
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Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT


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 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. We provide code in the supplementary.

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