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Engineering
1 of 13
Squeeze-and-Excitation Networks Mar 27, 2021 HeeDae Kwon
Contents • 1. Introduction • 2. Related Work • 3. Squeeze and Excitation Blocks • 4. Model And Computaional Complexity • 5. Experiments • 6. Ablation study
INTRODUCTION • Squeeze-and-Excitation - simple - computationally Lightweight - slight increase model complexity computational burnden
RELATED WORK • Deeper architectures • Algorithmic Architecture Search • Attention and gating mechanisms
SQUEEZE AND EXCITATION BLOCKS • Squeeze • Excitation
Model And Computaional Complexity Complexity = Performance
EXPERIMENTS TRAINING IMAGE VALIDATION IMAGE ERROR RATE OPTIMIZER LEARNING RATE EPOCH 1.28M 50K (from 1000 different classes) Top-1 ,Top-5 error rate SGD (momentum 0.9) 0.6 (decrease 10 every 30epochs) 100
EXPERIMENTS
EXPERIMENTS
EXPERIMENTS
ABLATION STUDY
ABLATION STUDT
• 감사합니다

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