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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons

February 1, 2023

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Authors

Byeongho Heo

Seoul National University


Minsik Lee

Hanyang University


Sangdoo Yun

NAVER Corp


Jin Young Choi

Seoul National University


DOI:

10.1609/aaai.v33i01.33013779


Abstract:

An activation boundary for a neuron refers to a separating hyperplane that determines whether the neuron is activated or deactivated. It has been long considered in neural networks that the activations of neurons, rather than their exact output values, play the most important role in forming classificationfriendly partitions of the hidden feature space. However, as far as we know, this aspect of neural networks has not been considered in the literature of knowledge transfer. In this paper, we propose a knowledge transfer method via distillation of activation boundaries formed by hidden neurons. For the distillation, we propose an activation transfer loss that has the minimum value when the boundaries generated by the student coincide with those by the teacher. Since the activation transfer loss is not differentiable, we design a piecewise differentiable loss approximating the activation transfer loss. By the proposed method, the student learns a separating boundary between activation region and deactivation region formed by each neuron in the teacher. Through the experiments in various aspects of knowledge transfer, it is verified that the proposed method outperforms the current state-of-the-art.

Topics: AAAI

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Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons Proceedings of the AAAI Conference on Artificial Intelligence (2019) 3779-3787.

Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons AAAI 2019, 3779-3787.

Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi (2019). Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons. Proceedings of the AAAI Conference on Artificial Intelligence, 3779-3787.

Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi. Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.3779-3787.

Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi. 2019. Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons. "Proceedings of the AAAI Conference on Artificial Intelligence". 3779-3787.

Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi. (2019) "Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons", Proceedings of the AAAI Conference on Artificial Intelligence, p.3779-3787

Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi, "Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons", AAAI, p.3779-3787, 2019.

Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi. "Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.3779-3787.

Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi. "Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 3779-3787.

Byeongho Heo||Minsik Lee||Sangdoo Yun||Jin Young Choi. Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons. AAAI[Internet]. 2019[cited 2023]; 3779-3787.


ISSN: 2374-3468


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