KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation Article Swipe
Mengqi Xue
,
Jie Song
,
Xinchao Wang
,
Ying Chen
,
Xingen Wang
,
Mingli Song
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2021/444
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2021/444
Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs). Despite the promising results achieved, the rationale that interprets the behavior of KD has yet remained largely understudied. In this paper, we introduce a novel task-oriented attention model, termed as KDExplainer, to shed light on the working mechanism underlying the vanilla KD. At the heart of KDExplainer is a Hierarchical Mixture of Experts (HME), in which a multi-class classification is reformulated as a multi-task binary one. Through distilling knowledge from a free-form pre-trained DNN to KDExplainer, we observe that KD implicitly modulates the knowledge conflicts between different subtasks, and in reality has much more to offer than label smoothing. Based on such findings, we further introduce a portable tool, dubbed as virtual attention module (VAM), that can be seamlessly integrated with various DNNs to enhance their performance under KD. Experimental results demonstrate that with a negligible additional cost, student models equipped with VAM consistently outperform their non-VAM counterparts across different benchmarks. Furthermore, when combined with other KD methods, VAM remains competent in promoting results, even though it is only motivated by vanilla KD. The code is available at https:// github.com/zju-vipa/KDExplainer.
Related Topics
Concepts
Computer science
Task (project management)
Scheme (mathematics)
Code (set theory)
Artificial intelligence
Smoothing
Class (philosophy)
Artificial neural network
Deep neural networks
Distillation
Binary number
Machine learning
Deep learning
Programming language
Arithmetic
Computer vision
Mathematical analysis
Set (abstract data type)
Management
Organic chemistry
Chemistry
Mathematics
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/ijcai.2021/444
- https://www.ijcai.org/proceedings/2021/0444.pdf
- OA Status
- gold
- Cited By
- 8
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3189953745
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3189953745Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24963/ijcai.2021/444Digital Object Identifier
- Title
-
KDExplainer: A Task-oriented Attention Model for Explaining Knowledge DistillationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-01Full publication date if available
- Authors
-
Mengqi Xue, Jie Song, Xinchao Wang, Ying Chen, Xingen Wang, Mingli SongList of authors in order
- Landing page
-
https://doi.org/10.24963/ijcai.2021/444Publisher landing page
- PDF URL
-
https://www.ijcai.org/proceedings/2021/0444.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.ijcai.org/proceedings/2021/0444.pdfDirect OA link when available
- Concepts
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Computer science, Task (project management), Scheme (mathematics), Code (set theory), Artificial intelligence, Smoothing, Class (philosophy), Artificial neural network, Deep neural networks, Distillation, Binary number, Machine learning, Deep learning, Programming language, Arithmetic, Computer vision, Mathematical analysis, Set (abstract data type), Management, Organic chemistry, Chemistry, Mathematics, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.id | doi:10.24963/ijcai.2021/444 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | https://www.ijcai.org/proceedings/2021/0444.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence |
| primary_location.landing_page_url | https://doi.org/10.24963/ijcai.2021/444 |
| publication_date | 2021-08-01 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2963122961, https://openalex.org/W2963370235, https://openalex.org/W2561238782, https://openalex.org/W2562979205, https://openalex.org/W3108643758, https://openalex.org/W3034756453, https://openalex.org/W2183341477, https://openalex.org/W2963777504, https://openalex.org/W3118608800, https://openalex.org/W3113223504, https://openalex.org/W1690739335, https://openalex.org/W2963534679, https://openalex.org/W2951527505, https://openalex.org/W2982242214, https://openalex.org/W2896457183, https://openalex.org/W3004809259, https://openalex.org/W1514535095, https://openalex.org/W2963140444, https://openalex.org/W3093410149, https://openalex.org/W2133564696, https://openalex.org/W1686810756, https://openalex.org/W2964111476, https://openalex.org/W3110791298, https://openalex.org/W2963125010, https://openalex.org/W2995607862, https://openalex.org/W2194775991, https://openalex.org/W2803023299, https://openalex.org/W1836465849, https://openalex.org/W3034795332, https://openalex.org/W2964137095, https://openalex.org/W2955192706, https://openalex.org/W2981413347, https://openalex.org/W2970726176, https://openalex.org/W1821462560, https://openalex.org/W2754084392 |
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| abstract_inverted_index.a | 40, 65, 73, 79, 87, 123, 151 |
| abstract_inverted_index.At | 59 |
| abstract_inverted_index.In | 35 |
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| abstract_inverted_index.as | 6, 46, 78, 127 |
| abstract_inverted_index.at | 194 |
| abstract_inverted_index.be | 134 |
| abstract_inverted_index.by | 187 |
| abstract_inverted_index.in | 71, 106, 178 |
| abstract_inverted_index.is | 64, 76, 184, 192 |
| abstract_inverted_index.it | 183 |
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| abstract_inverted_index.we | 38, 93, 120 |
| abstract_inverted_index.DNN | 90 |
| abstract_inverted_index.KD. | 58, 145, 189 |
| abstract_inverted_index.The | 190 |
| abstract_inverted_index.VAM | 159, 175 |
| abstract_inverted_index.and | 105 |
| abstract_inverted_index.can | 133 |
| abstract_inverted_index.for | 10 |
| abstract_inverted_index.has | 3, 30, 108 |
| abstract_inverted_index.the | 18, 22, 26, 52, 56, 60, 99 |
| abstract_inverted_index.yet | 31 |
| abstract_inverted_index.(KD) | 2 |
| abstract_inverted_index.DNNs | 139 |
| abstract_inverted_index.code | 191 |
| abstract_inverted_index.deep | 13 |
| abstract_inverted_index.even | 181 |
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| abstract_inverted_index.much | 109 |
| abstract_inverted_index.one. | 82 |
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| abstract_inverted_index.than | 113 |
| abstract_inverted_index.that | 24, 95, 132, 149 |
| abstract_inverted_index.this | 36 |
| abstract_inverted_index.when | 169 |
| abstract_inverted_index.with | 137, 150, 158, 171 |
| abstract_inverted_index.Based | 116 |
| abstract_inverted_index.cost, | 154 |
| abstract_inverted_index.heart | 61 |
| abstract_inverted_index.label | 114 |
| abstract_inverted_index.light | 50 |
| abstract_inverted_index.novel | 41 |
| abstract_inverted_index.offer | 112 |
| abstract_inverted_index.other | 172 |
| abstract_inverted_index.their | 142, 162 |
| abstract_inverted_index.tool, | 125 |
| abstract_inverted_index.under | 144 |
| abstract_inverted_index.which | 72 |
| abstract_inverted_index.(HME), | 70 |
| abstract_inverted_index.(VAM), | 131 |
| abstract_inverted_index.across | 165 |
| abstract_inverted_index.binary | 81 |
| abstract_inverted_index.dubbed | 126 |
| abstract_inverted_index.model, | 44 |
| abstract_inverted_index.models | 156 |
| abstract_inverted_index.module | 130 |
| abstract_inverted_index.neural | 14 |
| abstract_inverted_index.paper, | 37 |
| abstract_inverted_index.scheme | 9 |
| abstract_inverted_index.termed | 45 |
| abstract_inverted_index.though | 182 |
| abstract_inverted_index.(DNNs). | 16 |
| abstract_inverted_index.Despite | 17 |
| abstract_inverted_index.Experts | 69 |
| abstract_inverted_index.Mixture | 67 |
| abstract_inverted_index.Through | 83 |
| abstract_inverted_index.between | 102 |
| abstract_inverted_index.compact | 12 |
| abstract_inverted_index.emerged | 5 |
| abstract_inverted_index.enhance | 141 |
| abstract_inverted_index.further | 121 |
| abstract_inverted_index.largely | 33 |
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| abstract_inverted_index.reality | 107 |
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| abstract_inverted_index.virtual | 128 |
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| abstract_inverted_index.combined | 170 |
| abstract_inverted_index.equipped | 157 |
| abstract_inverted_index.https:// | 195 |
| abstract_inverted_index.learning | 11 |
| abstract_inverted_index.methods, | 174 |
| abstract_inverted_index.networks | 15 |
| abstract_inverted_index.portable | 124 |
| abstract_inverted_index.recently | 4 |
| abstract_inverted_index.remained | 32 |
| abstract_inverted_index.results, | 180 |
| abstract_inverted_index.Knowledge | 0 |
| abstract_inverted_index.achieved, | 21 |
| abstract_inverted_index.attention | 43, 129 |
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| abstract_inverted_index.competent | 177 |
| abstract_inverted_index.conflicts | 101 |
| abstract_inverted_index.different | 103, 166 |
| abstract_inverted_index.findings, | 119 |
| abstract_inverted_index.free-form | 88 |
| abstract_inverted_index.introduce | 39, 122 |
| abstract_inverted_index.knowledge | 85, 100 |
| abstract_inverted_index.mechanism | 54 |
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| abstract_inverted_index.promoting | 179 |
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| abstract_inverted_index.distilling | 84 |
| abstract_inverted_index.implicitly | 97 |
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| abstract_inverted_index.multi-task | 80 |
| abstract_inverted_index.negligible | 152 |
| abstract_inverted_index.outperform | 161 |
| abstract_inverted_index.seamlessly | 135 |
| abstract_inverted_index.smoothing. | 115 |
| abstract_inverted_index.underlying | 55 |
| abstract_inverted_index.KDExplainer | 63 |
| abstract_inverted_index.benchmarks. | 167 |
| abstract_inverted_index.demonstrate | 148 |
| abstract_inverted_index.efficacious | 8 |
| abstract_inverted_index.multi-class | 74 |
| abstract_inverted_index.performance | 143 |
| abstract_inverted_index.pre-trained | 89 |
| abstract_inverted_index.Experimental | 146 |
| abstract_inverted_index.Furthermore, | 168 |
| abstract_inverted_index.Hierarchical | 66 |
| abstract_inverted_index.KDExplainer, | 47, 92 |
| abstract_inverted_index.consistently | 160 |
| abstract_inverted_index.counterparts | 164 |
| abstract_inverted_index.distillation | 1 |
| abstract_inverted_index.reformulated | 77 |
| abstract_inverted_index.task-oriented | 42 |
| abstract_inverted_index.understudied. | 34 |
| abstract_inverted_index.classification | 75 |
| abstract_inverted_index.github.com/zju-vipa/KDExplainer. | 196 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.69379279 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |