Edge 451: In One Teacher Enough? Understanding Multi-Teacher Distillation
Was this email forwarded to you? Sign up here Edge 451: In One Teacher Enough? Understanding Multi-Teacher DistillationEnhancing the distillation process using more than one teacher.
In this issue:
💡 ML Concept of the Day: Understanding Multi-Teacher DistillationDistillation is typically explained using a teacher-student architecture, where we often conceptualize it as involving a single teacher model. However, there are many scenarios where this process can be enhanced by using multiple teachers. For example, a single-teacher model can produce biased students that focus on a limited form of knowledge. An alternative to the traditional approach is to use multiple teachers in a method known as multi-teacher distillation. A simple example of this approach might involve several teacher models, each specializing in a specific type of knowledge, such as feature-based or response-based information. Their outputs are then averaged to create the final student model. The result is a more robust student model, even though it is more costly to produce... 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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