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<h1><a href="(https://nn.labml.ai/distillation/index.html)">Distilling the Knowledge in a Neural Network</a></h1>
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation/tutorial of the paper
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<a href="https://papers.labml.ai/paper/1503.02531">Distilling the Knowledge in a Neural Network</a>.</p>
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<p>It&rsquo;s a way of training a small network using the knowledge in a trained larger network;
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i.e. distilling the knowledge from the large network.</p>
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<p>A large model with regularization or an ensemble of models (using dropout) generalizes
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better than a small model when trained directly on the data and labels.
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However, a small model can be trained to generalize better with help of a large model.
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Smaller models are better in production: faster, less compute, less memory.</p>
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<p>The output probabilities of a trained model give more information than the labels
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because it assigns non-zero probabilities to incorrect classes as well.
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These probabilities tell us that a sample has a chance of belonging to certain classes.
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For instance, when classifying digits, when given an image of digit <em>7</em>,
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a generalized model will give a high probability to 7 and a small but non-zero
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probability to 2, while assigning almost zero probability to other digits.
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Distillation uses this information to train a small model better.</p>
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<p><a href="https://app.labml.ai/run/d6182e2adaf011eb927c91a2a1710932"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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