SCIENTIA SINICA Informationis, Volume 47, Issue 11: 1483-1492(2017) https://doi.org/10.1360/N112017-00106

## Model reuse with domain knowledge

• AcceptedMay 22, 2017
• PublishedNov 14, 2017
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### Abstract

The life spans of machine learning models are often short and a large number of models are wasted because they can only be applied to a specific task. However, a well-designed, carefully trained model contains learned knowledge from its task, which may be more concise than training data. Furthermore, when we have no access to training data, the trained model is the last remaining source of information. This study introduces a framework to reuse existing models trained in other tasks and help improve the model for the current task, especially when limited data is available for the current task. This framework incorporates high-level domain knowledge to combine existing models and treat them as black boxes, in order for them to be universal for complex models. Experiments on applying the framework to practical problems demonstrate that we can improve the performance on the current task by reusing existing models.

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• Figure 1

The illustration of the MRDK framework

• Figure 2

The data and model flow of MRDK

• Figure 3

Part of the ancestor chart of endodeoxyribonuclease activity

• Table 1   Proteome datasets information
 Domain Proteome #Instance #Class Label cardinality Bacteria GEOSL 378 319 3.143 AZOVD 406 340 3.993 Archaea HALMA 304 234 3.247 PYRFU 425 321 4.480 Eukaryota YEAST 3507 1566 5.887
• Table 2   Results of protein function prediction
 Proteome Hamming loss $\downarrow$ F-measure $\uparrow$ $f_0$ $f^+$ $f_0$ $f^+$ GEOSL 0.070 0.023 0.064 0.124 AZOVD 0.096 0.024 0.035 0.071 HALMA 0.035 0.017 0.097 0.175 PYRFU 0.022 0.017 0.173 0.183 YEAST 0.108 0.009 0.012 0.080
• Table 3   Information of image datasets
 Dataset #Instance #Class Label cardinality Scene 2000 5 1.236 VOC07 9963 20 1.437 MS-COCO 122218 80 2.926 NUS-WIDE 133441 81 1.761
• Table 4   Results of image classification
 Dataset Hamming loss $\downarrow$ F-measure $\uparrow$ $f_0$ $f^+$ $f_0$ $f^+$ Scene 0.160 0.152 0.685 0.703 VOC07 0.056 0.047 0.623 0.665 MS-COCO 0.040 0.035 0.441 0.454 NUS-WIDE 0.035 0.029 0.198 0.196

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