Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation Article Swipe

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Chengchao Shen , Mengqi Xue , Xinchao Wang , Jie Song , Li Sun , Mingli Song ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1908.07121

A massive number of well-trained deep networks have been released by developers online. These networks may focus on different tasks and in many cases are optimized for different datasets. In this paper, we study how to exploit such heterogeneous pre-trained networks, known as teachers, so as to train a customized student network that tackles a set of selective tasks defined by the user. We assume no human annotations are available, and each teacher may be either single- or multi-task. To this end, we introduce a dual-step strategy that first extracts the task-specific knowledge from the heterogeneous teachers sharing the same sub-task, and then amalgamates the extracted knowledge to build the student network. To facilitate the training, we employ a selective learning scheme where, for each unlabelled sample, the student learns adaptively from only the teacher with the least prediction ambiguity. We evaluate the proposed approach on several datasets and experimental results demonstrate that the student, learned by such adaptive knowledge amalgamation, achieves performances even better than those of the teachers.

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Computer Science

Ambiguity

Artificial Intelligence

Machine Learning

Mathematical Analysis

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Computer science Ambiguity Exploit Task (project management) Set (abstract data type) Scheme (mathematics) Dual (grammatical number) Artificial intelligence Focus (optics) Sample (material) Machine learning Mathematical analysis Mathematics Optics Programming language Chromatography Art Chemistry Computer security Management Economics Physics Literature

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1908.07121
PDF: https://arxiv.org/pdf/1908.07121
OA Status: green
Cited By: 10
References: 36
Related Works: 10
OpenAlex ID: https://openalex.org/W2969933406

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DOI: https://doi.org/10.48550/arxiv.1908.07121

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Title: Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-08-20

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Authors: Chengchao Shen, Mengqi Xue, Xinchao Wang, Jie Song, Li Sun, Mingli Song

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Landing page: https://arxiv.org/abs/1908.07121

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PDF URL: https://arxiv.org/pdf/1908.07121

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Concepts: Computer science, Ambiguity, Exploit, Task (project management), Set (abstract data type), Scheme (mathematics), Dual (grammatical number), Artificial intelligence, Focus (optics), Sample (material), Machine learning, Mathematical analysis, Mathematics, Optics, Programming language, Chromatography, Art, Chemistry, Computer security, Management, Economics, Physics, Literature

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Cited by: 10

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Citations by year (recent): 2024: 3, 2023: 1, 2022: 1, 2021: 5

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abstract_inverted_index.massive	1
abstract_inverted_index.network	51
abstract_inverted_index.online.	12
abstract_inverted_index.results	150
abstract_inverted_index.sample,	126
abstract_inverted_index.several	146
abstract_inverted_index.sharing	97
abstract_inverted_index.single-	76
abstract_inverted_index.student	50, 110, 128
abstract_inverted_index.tackles	53
abstract_inverted_index.teacher	72, 134
abstract_inverted_index.achieves	161
abstract_inverted_index.adaptive	158
abstract_inverted_index.approach	144
abstract_inverted_index.datasets	147
abstract_inverted_index.evaluate	141
abstract_inverted_index.extracts	89
abstract_inverted_index.learning	120
abstract_inverted_index.network.	111
abstract_inverted_index.networks	6, 14
abstract_inverted_index.proposed	143
abstract_inverted_index.released	9
abstract_inverted_index.strategy	86
abstract_inverted_index.student,	154
abstract_inverted_index.teachers	96
abstract_inverted_index.datasets.	28
abstract_inverted_index.different	18, 27
abstract_inverted_index.dual-step	85
abstract_inverted_index.extracted	105
abstract_inverted_index.introduce	83
abstract_inverted_index.knowledge	92, 106, 159
abstract_inverted_index.networks,	40
abstract_inverted_index.optimized	25
abstract_inverted_index.selective	57, 119
abstract_inverted_index.sub-task,	100
abstract_inverted_index.teachers,	43
abstract_inverted_index.teachers.	169
abstract_inverted_index.training,	115
abstract_inverted_index.adaptively	130
abstract_inverted_index.ambiguity.	139
abstract_inverted_index.available,	69
abstract_inverted_index.customized	49
abstract_inverted_index.developers	11
abstract_inverted_index.facilitate	113
abstract_inverted_index.prediction	138
abstract_inverted_index.unlabelled	125
abstract_inverted_index.amalgamates	103
abstract_inverted_index.annotations	67
abstract_inverted_index.demonstrate	151
abstract_inverted_index.multi-task.	78
abstract_inverted_index.pre-trained	39
abstract_inverted_index.experimental	149
abstract_inverted_index.performances	162
abstract_inverted_index.well-trained	4
abstract_inverted_index.amalgamation,	160
abstract_inverted_index.heterogeneous	38, 95
abstract_inverted_index.task-specific	91
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	6
sustainable_development_goals[0].id	https://metadata.un.org/sdg/4
sustainable_development_goals[0].score	0.6899999976158142
sustainable_development_goals[0].display_name	Quality Education
citation_normalized_percentile