模型加速：WAE-Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks
WAE(Wavelet-like Auto-Encoder) 是由來自中大、哈工大、桂電等多機構的多名研究人員合作提出的，發表於AAAI-2018(論文地址： https://arxiv.org/pdf/1712.07493.pdf github 創新點： 1. WAE藉助小波分解得思想，將原圖分解成兩個低分辨率圖像，以達到網絡加速的目。 （PS：整體思路：下采樣的方法達到網絡加速，但是下采樣會導
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相關文章
- 1. 論文筆記：Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks
- 2. 模型加速--Slimmable neural networks
- 3. Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
- 4. 神經網絡壓縮（9） WAE-Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks
- 5. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 6. Improving Deep Neural Networks
- 7. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
- 8. [paper]Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
- 9. Neural Networks and Deep Learning
- 10. Improving Deep Neural Networks [1]