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Introduction
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Classroom Contents
A Review of Compression Methods for Deep Convolutional Neural Networks
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- 1 Introduction
- 2 Outline
- 3 Deep Learning
- 4 Problems with Deep Learning
- 5 Data Centers and Deep Learning
- 6 Layers
- 7 Data Sets
- 8 Questions
- 9 Architectures
- 10 Number of operations
- 11 Convolutional layers
- 12 Comparison of architectures
- 13 Comparing architectures
- 14 Retraining feature maps
- 15 Quantizing parameters
- 16 Quantization experiment
- 17 Flops rate
- 18 Compensation during training
- 19 Quantization during training
- 20 Quantization for precision
- 21 Results
- 22 Shift Attention Layers
- 23 Clustering weights
- 24 Energy consumption
- 25 Summary
- 26 Conclusion
- 27 Questions and Recap
