33 citations · 72 across the 8 of their papers we have counts for
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cs.CV2019★ 33 cited
Towards Efficient Training for Neural Network Quantization
Qing Jin, Linjie Yang, Zhenyu Liao
Quantization reduces computation costs of neural networks but suffers from performance degeneration. Is this accuracy drop due to the reduced capacity, or inefficient training duri…
cs.CV2019★ 17 cited
AdaBits: Neural Network Quantization with Adaptive Bit-Widths
Qing Jin, Linjie Yang, Zhenyu Liao
Deep neural networks with adaptive configurations have gained increasing attention due to the instant and flexible deployment of these models on platforms with different resource b…
cs.CV2019★ 18 cited
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
Yingwei Li, Song Bai, Cihang Xie +3
This paper focuses on learning transferable adversarial examples specifically against defense models (models to defense adversarial attacks). In particular, we show that a simple u…