Knowledge Distillation | Machine Learning

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We all know that ensembles outperform individual models. However, the increase in number of models does mean inference (evaluation of new data) is more costly. This is where knowledge distillation comes to the rescue... do watch to find out how!
  1. TwinEd Productions
  2. knowledge distillation
  3. machine learning
  4. ensembles
  5. deep learning
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Комментарии
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Very clear and concise with proper introduction!

softerseltzer
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Precise and to the point. Thank you for this awesome video.

prasundatta
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I am not sure to understand where the gain in training time is if the student has to learn from the teacher's predictions. Wouldn't it mean that we still have to train the large N x K model?

MeshRoun
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You said in a query that inference time reduces, training time remains same. How is that possible, could you explain?

kavyagupta
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Crisp and Clear! why we have used cross entropy function here?

aashishrana
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Awesome explanation mate, waiting for more videos!!!

wolfisraging
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Thank you! I was recently reading about this topic but was having trouble understanding. Your explanation was fantastic. Is knowledge distillation really just replacing the ground truth "hard" labels in the dataset with the teacher's soft labels?

victorsuciu
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Can you explain it a little more? How to select parameters for student architecture?

UmamahBintKhalid
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Imaging, we have.a three way
what????

xiangyangfrank