Knowledge Transfer via Dense Cross-Layer Mutual-Distillation
By Anbang Yao, Dawei Sun. 2020
70 citations recorded by the source corpus.
Knowledge Distillation (KD) based methods adopt the one-way Knowledge Transfer (KT) scheme in which training a lower-capacity student network is guided by a pre-trained high-capacity teacher network. Recently, Deep Mutual Learning (DML) presented a two-way KT strategy, showing that the student network can be also helpful to improve the teacher network. In this paper, we propose Dense Cross-layer Mutual-distillation (DCM), an improved two-way KT method in which the teacher and student networks are trained collaboratively from scratch. To augment knowledge representation learning, well-designed auxiliary classifiers are added to certain hidden layers of both teacher and student networks. To boost KT performance, we introduce dense bidirectional KD operations between the layers appended with classifiers. After training, all auxiliary classifiers are discarded, and thus there are no extra parameters introduced to final models. We test our method on a variety of KT tasks, showing its superiorities over related methods. Code is available at this https URL
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Vision and Pattern Recognition
Citations in the corpus
- Knowledge Distillation: A Survey (2020, background): “, 2018b) and DCM (Yao and Sun, 2020) are the performance of teacher and student after online distillation.”
- Collaborative Teacher-Student Learning via Multiple Knowledge Transfer (2021, background): “Collaborative learning is the one used most often [25, 29, 32, 8, 9, 7], where the teacher and the student as peer networks collaboratively teach and learn from each other, and the peer network architectures can be different.”