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Knowledge distillation

In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models ) have more knowledge capacity than small models, this capacity might not be fully utilized. It can be just as computationally…

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Methods

Concise knowledge representation Pseudolikelihoods Soft output Classification Softmax

17 related topics

History

Alexey Ivakhnenko USSR Regression analysis Jürgen Schmidhuber Recurrent neural networks

11 related topics

Overview

Machine learning Model Deep neural networks Ensembles Validity

11 related topics

Topics to explore

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Overview

Machine learning
Model Statistical model
Deep neural networks Deep neural network
Ensembles Ensemble learning
Validity Statistical model validation
Mobile device
Model compression
Object detection
Acoustic models Acoustic model
Natural language processing
Graph neural networks Graph neural network

Methods

Concise knowledge representation Concision
Pseudolikelihoods Pseudolikelihood
Soft output Soft-in soft-out decoder
Classification Statistical classification
Softmax Softmax function
Logit
Data set
Cross-entropy
Loss function
Entropy Entropy (information theory)
Variance
Gradient
Learning rate
Ground truth
Mean
Second-order backpropagation Backpropagation
Taylor expansion

History

Alexey Ivakhnenko
USSR
Regression analysis
Jürgen Schmidhuber
Recurrent neural networks Recurrent neural network
Deep learning
Statistical mechanics
Neural network
Preprint
Geoffrey Hinton
Image classification

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Map overview Semantic statistics

Knowledge distillation

Nodes43

Edges42

Triples10

Avg. degree1.95

Density0.046512

Components1

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Knowledge distillation

Top relations

related to Mathematical formulation · 3

Knowledge distillation → Given, Knowledge, The

related to Relationship with model compression · 3

Knowledge distillation → For, The, Under

Important terminology Word statistics

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Important terminology

model knowledge large distillation smaller compression loss one models displaystyle neural network parameter networks output learning also training data capacity

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
object detection	instance of	while decreasing the bits-per-parameter.Knowledge distillation has been successfully used in several applications of machine learning	0.80	text
acoustic models	instance of	while decreasing the bits-per-parameter.Knowledge distillation has been successfully used in several applications of machine learning	0.80	text
and natural language processing	instance of	while decreasing the bits-per-parameter.Knowledge distillation has been successfully used in several applications of machine learning	0.80	text
backpropagation	instance of	by methods	0.80	text
Knowledge distillation	related to Mathematical formulation	Given	0.60	section
Knowledge distillation	related to Mathematical formulation	The	0.60	section
Knowledge distillation	related to Mathematical formulation	Knowledge	0.60	section
Knowledge distillation	related to Relationship with model compression	Under	0.60	section
Knowledge distillation	related to Relationship with model compression	The	0.60	section
Knowledge distillation	related to Relationship with model compression	For	0.60	section

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