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Multi scale dense networks

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Multi-scale Dense Networks for Resource Efficient Image Classification

Published in: Science
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Multi scale dense networks

  1. 1. MULTI-SCALE DENSE NETWORKS FOR RESOURCE EFFICIENT IMAGE CLASSIFICATION Gao Huang et al.,ICLR 2018(Oral)
  2. 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. 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. 4. Motivation • Problems • The lack of coarse-level features of early-exit classifiers • Early classifiers interfere with later classifier
  5. 5. Multi-Scale DenseNet (MSDNet) • Solutions • Multi-scale feature maps • Dense connectivity
  6. 6. Multi-Scale DenseNet (MSDNet) • Architecture • First Layer • Subsequent layers • Classifiers : conv + conv + average pooling + linear layer • Loss Functions : cross entropy loss
  7. 7. Multi-Scale DenseNet (MSDNet) • Network reduction S – i + 1
  8. 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. 9. Multi-Scale DenseNet (MSDNet) • Experiments • Anytime Prediction
  10. 10. Multi-Scale DenseNet (MSDNet) • Experiments • Budged batch classification
  11. 11. Multi-Scale DenseNet (MSDNet) • Visualization

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