Aug 20, 2022 · In this paper, we propose the DenseShift network, which significantly improves the accuracy of Shift networks, achieving competitive performance ...

In this paper, we pro- pose the DenseShift network, which significantly improves the accuracy of Shift networks, achieving competitive per- formance to full- ...

Aug 20, 2022 · Abstract. Deploying deep neural networks on low-resource edge devices is challenging due to their ever-increasing resource requirements.

The experimental results show that DenseShift network significantly outperforms existing low-bit multiplication-free networks and can achieve competitive ...

The experimental results show that DenseShift network significantly outperforms existing low-bit multiplication-free networks and can achieve competitive ...

In this paper, we propose the DenseShift network, which significantly improves the accuracy of Shift networks, achieving competitive performance to full- ...

Xinlin Li, Bang Liu, Rui Heng Yang, Vanessa Courville, Chao Xing, Vahid Partovi Nia: DenseShift: Towards Accurate and Transferable Low-Bit Shift Network.

DenseShift: Towards Accurate and Transferable Low-Bit Shift Network. X. Li, B. Liu, R. Yang, V. Courville, C. Xing, and V. Nia. CoRR, (2022 ).

DenseShift: Towards Accurate and Transferable Low-Bit Shift Network ... Deploying deep neural networks on low-resource edge devices is challenging due to their ...

DenseShift: Towards Accurate and Transferable Low-Bit Shift Network. X Li, B Liu, RH Yang, V Courville, C Xing, VP Nia. arXiv preprint arXiv:2208.09708, 2022.