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智計 Deep Learning Methods for Application-driven
慧
畫 Smart System Integration
Arti cial Intelligence Projects Principal Investigator: Dr. Chu-Song Chen; Co-PIs: Dr. Hsin-Min Wang, and Dr. Lun-Wei Ku
Project Period: 2018/1~2021/12
Deep neural networks have played a primary role in recent However, it is often di cult to know in advance what might
advances in AI. Multiple deep learning models have been be a suitable model architecture for learning all of the
designed to handle various tasks. Since those models are tasks well. In this project, we are developing an approach
trained with particular datasets, they are only effective whereby di erent deep learning models are integrated by
for specific purposes. Our objective is to integrate these removing the redundancies among their lters or weights.
di erent deep learning models (each trained with speci c Unlike existing approaches that conduct this step only in
data). Our integrated network model will be capable of the training stage, our method integrates multiple models
handling simultaneously all of the individual tasks of the at the inference stage. The resulting merged model can
original models. Integrated deep learning models of this be further ne-tuned using the training data if necessary.
nature have huge potential in real-world applications, We are also developing tools for continuous deep learning
such as in AI agents or robots that are required to conduct and integration into merged models, so that the multi-
multiple recognition tasks based on the same or di erent tasking of merged models can be continuously expanded.
signal sources (e.g., images, sounds). Even where only We anticipate our merged model will be capable of
image signals are interpreted, a deep learning smart system integrating image, audio, and natural language processing
may have to process various visual classi cation tasks (such tasks. In addition, we are also establishing deep learning
as object recognition, face identification, hand gesture methods for efficient binary feature representations,
prediction). thereby enabling rapid retrieval or recall from a large
database. The project has two main aspects: (I) Integration
A typical approach to tackling multiple recognition tasks of heterogeneous deep models, and (II) Deep learning
in a single system is to design a new model and train that models for smart system applications. We discuss some of
new model on the combined datasets for all of the tasks. our results thus far below.
I. Integration of Heterogeneous Deep Models
Unifying and Merging Well-trained Deep Neural Networks at the Inference Stage
We are proposing a novel approach to merging is substantially reduced because our method leverages the
convolutional neural networks at the inference stage. weights shared among individual models and preserves the
Our method can align the layers of two feed-forward general architectures of their well-trained neural networks.
neural networks already trained to handle different tasks The resulting merged model is jointly compressed and
and merge them into a unified model by sharing their can be implemented faster than the original models but
representative weights. The performance of the resulting has comparable accuracy to them. Our results have been
merged model can be improved by retraining. Our published in IJCAI 2018. We have also applied this approach
approach effectively produces a compact model that can to merging and co-compressing face-recognition and
simultaneously undertake the original tasks of individual speaker-identification in a single compact model, which
models on resource-limited hardware. The development was presented at the CVPR 2019 Workshop on Multimodal
time for the merged model, as well as training overheads, Learning and Applications.
Figure 1 : Co-compression of deep CNN models. Our approach can merge well-trained deep CNN models
into a more compact model for e cient inference.
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智計 Deep Learning Methods for Application-driven
慧
畫 Smart System Integration
Arti cial Intelligence Projects Principal Investigator: Dr. Chu-Song Chen; Co-PIs: Dr. Hsin-Min Wang, and Dr. Lun-Wei Ku
Project Period: 2018/1~2021/12
Deep neural networks have played a primary role in recent However, it is often di cult to know in advance what might
advances in AI. Multiple deep learning models have been be a suitable model architecture for learning all of the
designed to handle various tasks. Since those models are tasks well. In this project, we are developing an approach
trained with particular datasets, they are only effective whereby di erent deep learning models are integrated by
for specific purposes. Our objective is to integrate these removing the redundancies among their lters or weights.
di erent deep learning models (each trained with speci c Unlike existing approaches that conduct this step only in
data). Our integrated network model will be capable of the training stage, our method integrates multiple models
handling simultaneously all of the individual tasks of the at the inference stage. The resulting merged model can
original models. Integrated deep learning models of this be further ne-tuned using the training data if necessary.
nature have huge potential in real-world applications, We are also developing tools for continuous deep learning
such as in AI agents or robots that are required to conduct and integration into merged models, so that the multi-
multiple recognition tasks based on the same or di erent tasking of merged models can be continuously expanded.
signal sources (e.g., images, sounds). Even where only We anticipate our merged model will be capable of
image signals are interpreted, a deep learning smart system integrating image, audio, and natural language processing
may have to process various visual classi cation tasks (such tasks. In addition, we are also establishing deep learning
as object recognition, face identification, hand gesture methods for efficient binary feature representations,
prediction). thereby enabling rapid retrieval or recall from a large
database. The project has two main aspects: (I) Integration
A typical approach to tackling multiple recognition tasks of heterogeneous deep models, and (II) Deep learning
in a single system is to design a new model and train that models for smart system applications. We discuss some of
new model on the combined datasets for all of the tasks. our results thus far below.
I. Integration of Heterogeneous Deep Models
Unifying and Merging Well-trained Deep Neural Networks at the Inference Stage
We are proposing a novel approach to merging is substantially reduced because our method leverages the
convolutional neural networks at the inference stage. weights shared among individual models and preserves the
Our method can align the layers of two feed-forward general architectures of their well-trained neural networks.
neural networks already trained to handle different tasks The resulting merged model is jointly compressed and
and merge them into a unified model by sharing their can be implemented faster than the original models but
representative weights. The performance of the resulting has comparable accuracy to them. Our results have been
merged model can be improved by retraining. Our published in IJCAI 2018. We have also applied this approach
approach effectively produces a compact model that can to merging and co-compressing face-recognition and
simultaneously undertake the original tasks of individual speaker-identification in a single compact model, which
models on resource-limited hardware. The development was presented at the CVPR 2019 Workshop on Multimodal
time for the merged model, as well as training overheads, Learning and Applications.
Figure 1 : Co-compression of deep CNN models. Our approach can merge well-trained deep CNN models
into a more compact model for e cient inference.
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