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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Hierarchical Knowledge Squeezed Adversarial Network Compression

February 1, 2023

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Authors

Peng Li

East China Normal University


Chang Shu

East China Normal University


Yuan Xie

East China Normal University


Yan Qu

Xiamen University


Hui Kong

Horizon Robotics


DOI:

10.1609/aaai.v34i07.6799


Abstract:

Deep network compression has been achieved notable progress via knowledge distillation, where a teacher-student learning manner is adopted by using predetermined loss. Recently, more focuses have been transferred to employ the adversarial training to minimize the discrepancy between distributions of output from two networks. However, they always emphasize on result-oriented learning while neglecting the scheme of process-oriented learning, leading to the loss of rich information contained in the whole network pipeline. Whereas in other (non GAN-based) process-oriented methods, the knowledge have usually been transferred in a redundant manner. Observing that, the small network can not perfectly mimic a large one due to the huge gap of network scale, we propose a knowledge transfer method, involving effective intermediate supervision, under the adversarial training framework to learn the student network. Different from the other intermediate supervision methods, we design the knowledge representation in a compact form by introducing a task-driven attention mechanism. Meanwhile, to improve the representation capability of the attention-based method, a hierarchical structure is utilized so that powerful but highly squeezed knowledge is realized and the knowledge from teacher network could accommodate the size of student network. Extensive experimental results on three typical benchmark datasets, i.e., CIFAR-10, CIFAR-100, and ImageNet, demonstrate that our method achieves highly superior performances against state-of-the-art methods.

Topics: AAAI

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HOW TO CITE:

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong Hierarchical Knowledge Squeezed Adversarial Network Compression Proceedings of the AAAI Conference on Artificial Intelligence (2020) 11370-11377.

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong Hierarchical Knowledge Squeezed Adversarial Network Compression AAAI 2020, 11370-11377.

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong (2020). Hierarchical Knowledge Squeezed Adversarial Network Compression. Proceedings of the AAAI Conference on Artificial Intelligence, 11370-11377.

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong. Hierarchical Knowledge Squeezed Adversarial Network Compression. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.11370-11377.

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong. 2020. Hierarchical Knowledge Squeezed Adversarial Network Compression. "Proceedings of the AAAI Conference on Artificial Intelligence". 11370-11377.

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong. (2020) "Hierarchical Knowledge Squeezed Adversarial Network Compression", Proceedings of the AAAI Conference on Artificial Intelligence, p.11370-11377

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong, "Hierarchical Knowledge Squeezed Adversarial Network Compression", AAAI, p.11370-11377, 2020.

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong. "Hierarchical Knowledge Squeezed Adversarial Network Compression". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.11370-11377.

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong. "Hierarchical Knowledge Squeezed Adversarial Network Compression". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 11370-11377.

Peng Li||Chang Shu||Yuan Xie||Yan Qu||Hui Kong. Hierarchical Knowledge Squeezed Adversarial Network Compression. AAAI[Internet]. 2020[cited 2023]; 11370-11377.


ISSN: 2374-3468


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