Self-Supervised GAN Compression Article Swipe
Chong Yu
,
Jeff Pool
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.01491
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.01491
Deep learning's success has led to larger and larger models to handle more and more complex tasks; trained models can contain millions of parameters. These large models are compute- and memory-intensive, which makes it a challenge to deploy them with minimized latency, throughput, and storage requirements. Some model compression methods have been successfully applied to image classification and detection or language models, but there has been very little work compressing generative adversarial networks (GANs) performing complex tasks. In this paper, we show that a standard model compression technique, weight pruning, cannot be applied to GANs using existing methods. We then develop a self-supervised compression technique which uses the trained discriminator to supervise the training of a compressed generator. We show that this framework has a compelling performance to high degrees of sparsity, can be easily applied to new tasks and models, and enables meaningful comparisons between different pruning granularities.
Related Topics
Concepts
Discriminator
Computer science
Pruning
Generator (circuit theory)
Generative grammar
Compression (physics)
Artificial intelligence
Language model
Machine learning
Data compression
Generative adversarial network
Deep learning
Power (physics)
Biology
Materials science
Composite material
Agronomy
Telecommunications
Detector
Physics
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2007.01491
- https://arxiv.org/pdf/2007.01491
- OA Status
- green
- Cited By
- 4
- References
- 42
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2998257508
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2998257508Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2007.01491Digital Object Identifier
- Title
-
Self-Supervised GAN CompressionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-03Full publication date if available
- Authors
-
Chong Yu, Jeff PoolList of authors in order
- Landing page
-
https://arxiv.org/abs/2007.01491Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2007.01491Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2007.01491Direct OA link when available
- Concepts
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Discriminator, Computer science, Pruning, Generator (circuit theory), Generative grammar, Compression (physics), Artificial intelligence, Language model, Machine learning, Data compression, Generative adversarial network, Deep learning, Power (physics), Biology, Materials science, Composite material, Agronomy, Telecommunications, Detector, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 3, 2020: 1Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2981972129, https://openalex.org/W2962968458, https://openalex.org/W2963684088, https://openalex.org/W2962883174, https://openalex.org/W2912851681, https://openalex.org/W2963723139, https://openalex.org/W2899771611, https://openalex.org/W2099471712, https://openalex.org/W2962965870, https://openalex.org/W2944870588, https://openalex.org/W2114770744, https://openalex.org/W2963470893, https://openalex.org/W2893749619, https://openalex.org/W2741137940, https://openalex.org/W2903135068, https://openalex.org/W2964299589, https://openalex.org/W2921077359, https://openalex.org/W2963847166, https://openalex.org/W2064675550, https://openalex.org/W2611493949, https://openalex.org/W1821462560, https://openalex.org/W2963000224, https://openalex.org/W2963981733, https://openalex.org/W2625457103, https://openalex.org/W2788836009, https://openalex.org/W2739748921, https://openalex.org/W2962793481, https://openalex.org/W3034733718, https://openalex.org/W2924515500, https://openalex.org/W2963643655, https://openalex.org/W2172166488, https://openalex.org/W2914221386, https://openalex.org/W1834627138, https://openalex.org/W2157331557, https://openalex.org/W2963112338, https://openalex.org/W2963767194, https://openalex.org/W2133665775, https://openalex.org/W2963674932, https://openalex.org/W2934222362, https://openalex.org/W2900694899, https://openalex.org/W2047920195, https://openalex.org/W2914366177 |
| referenced_works_count | 42 |
| abstract_inverted_index.a | 34, 83, 101, 115, 124 |
| abstract_inverted_index.In | 77 |
| abstract_inverted_index.We | 98, 118 |
| abstract_inverted_index.be | 91, 133 |
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| abstract_inverted_index.and | 7, 13, 29, 43, 57, 139, 141 |
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| abstract_inverted_index.which | 31, 105 |
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| abstract_inverted_index.models | 9, 18, 26 |
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| abstract_inverted_index.tasks; | 16 |
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| abstract_inverted_index.language | 60 |
| abstract_inverted_index.latency, | 41 |
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| abstract_inverted_index.networks | 72 |
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| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.61069337 |
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| citation_normalized_percentile.is_in_top_10_percent | False |