EnergyNet: Energy-Efficient Dynamic Inference
By Yue Wang, Tan Minh Nguyen, Tan Y. Nguyen, Yang Zhao, Zhangyang Wang, Yingyan Lin, Richard Baraniuk. 2018
21 citations recorded by the source corpus.
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Citations in the corpus
- AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks (2020, neutral): “These encoder-decoder GANs also gain popularity in image enhancement (Wang et al., 2018a; Kupyn et al., 2019; Jiang et al., 2019) and editing (Sanakoyeu et al., 2018; Yang et al., 2019).”
- Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference (2020, methodology): “We evaluate the proposed DFS against the competitive dynamic inference technique SkipNet (Wang et al. 2018a) and two state-of-the-art static CNN quantization techniques proposed in (Banner et al. 2018) and (Wang et al. 2019).”
- Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference (2020, methodology): “Layer factorization (Tai et al., 2016; Yu et al., 2017), quantization (Wu et al., 2016), model distillation (Wang et al., 2018c) and weight sharing (Wu et al., 2018) have also been respectively found effective.”
- Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference (2019, unknown): “memory accesses [46].”
- Drawing early-bird tickets: Towards more efficient training of deep networks (2019, unknown): “(Banner et al., 2018; Wang et al., 2018a) , where θ t denotes the weights at the t -th iteration of training.”