Edge 447: Not All Model Distillations are Created Equal
Was this email forwarded to you? Sign up here Edge 447: Not All Model Distillations are Created EqualUnderstanding the different types of model distillation.
In this issue:
💡 ML Concept of the Day: Types of Model DistillationIn the previous issue of this series, we introduced the concept of model distillation. The core idea of this technique is to use a teacher model to train a smaller, more efficient model that maintains the accuracy of the teacher model on a specific set of domains. As you can imagine, there are multiple ways to accomplish this based on a teacher-student interactions. Model distillation techniques fall into three primary categories: response-based, feature-based, and relation-based. Each method emphasizes a different aspect of transferring knowledge from a larger, more complex teacher model to a smaller, more efficient student model. These approaches each come with their own set of strengths and limitations, and understanding them can be beneficial, even though response-based distillation is often the easiest to implement. 1. Response-Based Distillation:... Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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