1. MULTI-SCALE DENSE NETWORKS
FOR RESOURCE EFFICIENT IMAGE CLASSIFICATION
Gao Huang et al.，ICLR 2018（Oral）

2. Motivation
• Two Settings:
• Anytime classification
• Budgeted batch classification
• A test image
• “easy” vs. “hard”
• shallow model vs. deep model
• Training multiple classifiers with varying
resource demands, which we adaptively
apply during test time.

3. Motivation
• Develop CNNs
• “slice” the computation and process these slices one-by-one, stopping the
evaluation once the CPU time is depleted or the classification sufficiently
certain (through “early exits”)
classifier
…feature

4. Motivation
• Problems
• The lack of coarse-level features of early-exit classifiers
• Early classifiers interfere with later classifier

5. Multi-Scale DenseNet (MSDNet)
• Solutions
• Multi-scale feature maps
• Dense connectivity

6. Multi-Scale DenseNet (MSDNet)
• Architecture
• First Layer
• Subsequent layers
• Classifiers : conv + conv + average pooling + linear layer
• Loss Functions : cross entropy loss

7. Multi-Scale DenseNet (MSDNet)
• Network reduction
S – i + 1

8. Multi-Scale DenseNet (MSDNet)
• Two Settings:
• Anytime classification – output the most recent prediction until the budget is
exhausted
• Budgeted batch classification – exits after classifier fk if its prediction
confidence exceeds a pre-determined threshold

9. Multi-Scale DenseNet (MSDNet)
• Experiments
• Anytime Prediction

10. Multi-Scale DenseNet (MSDNet)
• Experiments
• Budged batch classification

11. Multi-Scale DenseNet (MSDNet)
• Visualization