Distilling Knowledge via Knowledge Review Article Swipe
Pengguang Chen
,
Shu Liu
,
Hengshuang Zhao
,
Jiaya Jia
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.09044
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.09044
Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. We differently study the factor of connection path cross levels between teacher and student networks, and reveal its great importance. For the first time in knowledge distillation, cross-stage connection paths are proposed. Our new review mechanism is effective and structurally simple. Our finally designed nested and compact framework requires negligible computation overhead, and outperforms other methods on a variety of tasks. We apply our method to classification, object detection, and instance segmentation tasks. All of them witness significant student network performance improvement. Code is available at https://github.com/Jia-Research-Lab/ReviewKD
Related Topics
Concepts
Computer science
Overhead (engineering)
Variety (cybernetics)
Path (computing)
Focus (optics)
Code (set theory)
Feature (linguistics)
Transformation (genetics)
Distillation
Construct (python library)
Artificial intelligence
Machine learning
Programming language
Linguistics
Set (abstract data type)
Physics
Organic chemistry
Gene
Chemistry
Biochemistry
Optics
Philosophy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2104.09044
- https://arxiv.org/pdf/2104.09044
- OA Status
- green
- Cited By
- 11
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3154971402
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3154971402Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2104.09044Digital Object Identifier
- Title
-
Distilling Knowledge via Knowledge ReviewWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-04-19Full publication date if available
- Authors
-
Pengguang Chen, Shu Liu, Hengshuang Zhao, Jiaya JiaList of authors in order
- Landing page
-
https://arxiv.org/abs/2104.09044Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2104.09044Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2104.09044Direct OA link when available
- Concepts
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Computer science, Overhead (engineering), Variety (cybernetics), Path (computing), Focus (optics), Code (set theory), Feature (linguistics), Transformation (genetics), Distillation, Construct (python library), Artificial intelligence, Machine learning, Programming language, Linguistics, Set (abstract data type), Physics, Organic chemistry, Gene, Chemistry, Biochemistry, Optics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 6, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2104.09044 |
| publication_date | 2021-04-19 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2964233199, https://openalex.org/W2981794819, https://openalex.org/W1686810756, https://openalex.org/W2951569836, https://openalex.org/W2951513317, https://openalex.org/W2786945063, https://openalex.org/W2612445135, https://openalex.org/W2944779197, https://openalex.org/W2796438033, https://openalex.org/W2939524044, https://openalex.org/W2752782242, https://openalex.org/W2805003733, https://openalex.org/W2952596663, https://openalex.org/W2963125010, https://openalex.org/W2953106684, https://openalex.org/W2743473392, https://openalex.org/W2108598243, https://openalex.org/W2949533892, https://openalex.org/W1821462560, https://openalex.org/W2194775991, https://openalex.org/W2964161024, https://openalex.org/W2561238782, https://openalex.org/W2739879705 |
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